Digital data sources and methods for conservation culturomics.

Ongoing loss of biological diversity is primarily the result of unsustainable human behavior. Thus, the long-term success of biodiversity conservation depends on a thorough understanding of human-nature interactions. Such interactions are ubiquitous but vary greatly in time and space and are difficult to monitor efficiently at large spatial scales. However, the Information Age also provides new opportunities to better understand human-nature interactions because many aspects of daily life are recorded in a variety of digital formats. The emerging field of conservation culturomics aims to take advantage of digital data sources and methods to study human-nature interactions and thus to provide new tools for studying conservation at relevant temporal and spatial scales. Nevertheless, technical challenges associated with the identification, access, and analysis of relevant data hamper the wider adoption of culturomics methods. To help overcome these barriers, we propose a conservation culturomics research framework that addresses data acquisition, analysis, and inherent biases. The main sources of culturomic data include web pages, social media, and other digital platforms from which metrics of content and engagement can be obtained. Obtaining raw data from these platforms is usually desirable but requires careful consideration of how to access, store, and prepare the data for analysis. Methods for data analysis include network approaches to explore connections between topics, time-series analysis for temporal data, and spatial modeling to highlight spatial patterns. Outstanding challenges associated with culturomics research include issues of interdisciplinarity, ethics, data biases, and validation. The practical guidance we offer will help conservation researchers and practitioners identify and obtain the necessary data and carry out appropriate analyses for their specific questions, thus facilitating the wider adoption of culturomics approaches for conservation applications.

Fuentes de Información Digital y Métodos para la Culturomia de la Conservación Resumen La continua pérdida de biodiversidad es el resultado principal del comportamiento humano insostenible. Por esto, el éxito a largo plazo de la conservación de la biodiversidad depende de una comprensión exhaustiva de las interacciones humano-naturaleza. Dichas interacciones son ubicuas pero varían enormemente en el tiempo y el espacio, lo que dificulta su monitoreo eficiente a escalas espaciales amplias. Sin embargo, la Era de la Información también nos proporciona nuevas oportunidades para comprender de mejor manera las interacciones humano-naturaleza pues muchos aspectos de la vida diaria quedan registrados en una variedad de formatos digitales. El campo emergente de la culturomia de la conservación busca aprovechar los recursos y los métodos digitales para estudiar las interacciones humano-naturaleza y así proporcionar nuevas herramientas para el estudio de la conservación a escalas temporales y espaciales relevantes. No obstante, las dificultades técnicas asociadas con la identificación, acceso y análisis de la información relevante obstaculizan la adopción más amplia de los métodos de la culturomia. Para ayudar a superar estas barreras proponemos un marco de trabajo de investigación de culturomia de la conservación que aborde la obtención de datos, el análisis y los sesgos inherentes. Entre las principales fuentes de datos sobre culturomia se incluyen las páginas web, las redes sociales y otras plataformas digitales a partir de las cuales se pueden obtener medidas del contenido y la participación. Normalmente se busca obtener datos crudos a partir de este tipo de plataformas, pero esto requiere que se tengan en consideración las vías de acceso, el almacenaje y la preparación de la información para su posterior análisis. Los métodos para el análisis de datos incluyen analísis de redes para explorar las conexiones entre los temas, el análisis de series de tiempo para los datos temporales y el modelado espacial para resaltar los patrones espaciales. Los desafíos sobresalientes asociados a la investigación en culturomia incluyen temas de interdisciplinariedad, ética, sesgos de datos y validación. La orientación práctica que ofrecemos ayudará a los investigadores y practicantes de la conservación a identificar y obtener los datos necesarios. También les ayudará a realizar análisis apropiados para responder a sus preguntas específicas, facilitando así la adopción más amplia de las estrategias de culturomia para su aplicación en la conservación.

How to address data privacy concerns when using social media data in conservation science.

Social media data are being increasingly used in conservation science to study human-nature interactions. User-generated content, such as images, video, text, and audio, and the associated metadata can be used to assess such interactions. A number of social media platforms provide free access to user-generated social media content. However, similar to any research involving people, scientific investigations based on social media data require compliance with highest standards of data privacy and data protection, even when data are publicly available. Should social media data be misused, the risks to individual users' privacy and well-being can be substantial. We investigated the legal basis for using social media data while ensuring data subjects' rights through a case study based on the European Union's General Data Protection Regulation. The risks associated with using social media data in research include accidental and purposeful misidentification that has the potential to cause psychological or physical harm to an identified person. To collect, store, protect, share, and manage social media data in a way that prevents potential risks to users involved, one should minimize data, anonymize data, and follow strict data management procedure. Risk-based approaches, such as a data privacy impact assessment, can be used to identify and minimize privacy risks to social media users, to demonstrate accountability and to comply with data protection legislation. We recommend that conservation scientists carefully consider our recommendations in devising their research objectives so as to facilitate responsible use of social media data in conservation science research, for example, in conservation culturomics and investigations of illegal wildlife trade online.

Cómo Abordar las Preocupaciones por Privacidad al Usar las Redes Sociales en las Ciencias de las Conservación Resumen Cada vez se usan más los datos de las redes sociales en las ciencias de la conservación para estudiar las interacciones humano-naturaleza. El contenido generado por usuarios (imágenes, videos, textos y audios) y los metadatos asociados a estos pueden usarse para evaluar dichas interacciones. Un gran número de redes sociales proporcionan acceso gratuito al contenido generado por usuarios en las redes sociales. Sin embargo, como con cualquier investigación que involucre personas, las investigaciones basadas en los datos obtenidos de la redes sociales requieren cumplir con los estándares más altos de privacidad de datos y protección de la información, incluso cuando éstos están disponibles públicamente. En caso de que se le dé un uso inapropiado a la información obtenida de las redes sociales, los riesgos para la privacidad del usuario y para su bienestar pueden ser sustanciales. Investigamos las bases legales para el uso de la información de redes sociales en conjunto con la garantía de derechos para los sujetos de la información por medio de un estudio de caso basado en la Regulación de la Protección de Datos Generales (GDPR) de la Unión Europea (EU). Los riesgos asociados con el uso de información de las redes sociales en la investigación incluyen la identificación errónea accidental o intencional, la cual tiene el potencial de ocasionar daño psicológico o físico a la persona identificada. Para recolectar, almacenar, proteger, compartir y administrar la información de las redes sociales de manera que se prevengan los riesgos potenciales para los usuarios involucrados, se deben minimizar los datos, volverlos anónimos y seguir un procedimiento estricto de manejo de datos. Las estrategias basadas en riesgos, como la evaluación del impacto de la privacidad de datos, pueden usarse para identificar y minimizar los riesgos de privacidad presentes para los usuarios de las redes, para demostrar responsabilidades y para cumplir con la legislación de protección de datos. Recomendamos a los científicos de la conservación que consideren con cuidado nuestras recomendaciones para el diseño de sus objetivos de investigación para así facilitar el uso responsable de la información de redes sociales en la investigación de las ciencias de la conservación, por ejemplo para las investigaciones sobre el mercado ilegal de fauna en línea y para la culturomia de la conservación.

Assessing global popularity and threats to Important Bird and Biodiversity Areas using social media data.

Understanding worldwide patterns of human use of sites of international significance for biodiversity conservation is crucial for meeting global conservation targets. However, robust global datasets are scarce. In this study, we used social media data, mined from Flickr and Twitter, geolocated in Important Bird and Biodiversity Areas (IBAs) to assess i) patterns of popularity; ii) relationships of this popularity with geographical and biological variables; and iii) identify sites under high pressure from visitors. IBAs located in Europe and Asia, and in temperate biomes, had the highest density of users. Sites of importance for congregatory species, which were also more accessible, more densely populated and provided more tourism facilities, received higher visitation than did sites richer in bird species. We found 17% of all IBAs assessed to be under very high threat also received high visitation. Our results show in which IBAs enhanced monitoring should be implemented to reduce potential visitation risks to sites of conservation concern for birds, and to harness the potential benefits of tourism for conservation.

Instagram, Flickr, or Twitter: Assessing the usability of social media data for visitor monitoring in protected areas.

Social media data is increasingly used as a proxy for human activity in different environments, including protected areas, where collecting visitor information is often laborious and expensive, but important for management and marketing. Here, we compared data from Instagram, Twitter and Flickr, and assessed systematically how park popularity and temporal visitor counts derived from social media data perform against high-precision visitor statistics in 56 national parks in Finland and South Africa in 2014. We show that social media activity is highly associated with park popularity, and social media-based monthly visitation patterns match relatively well with the official visitor counts. However, there were considerable differences between platforms as Instagram clearly outperformed Twitter and Flickr. Furthermore, we show that social media data tend to perform better in more visited parks, and should always be used with caution. Based on stakeholder discussions we identified potential reasons why social media data and visitor statistics might not match: the geography and profile of the park, the visitor profile, and sudden events. Overall the results are encouraging in broader terms: Over 60% of the national parks globally have Twitter or Instagram activity, which could potentially inform global nature conservation.

Social media reveal that charismatic species are not the main attractor of ecotourists to sub-Saharan protected areas.

Charismatic megafauna are arguably considered the primary attractor of ecotourists to sub-Saharan African protected areas. However, the lack of visitation data across the whole continent has thus far prevented the investigation of whether charismatic species are indeed a key attractor of ecotourists to protected areas. Social media data can now be used for this purpose. We mined data from Instagram, and used generalized linear models with site- and country-level deviations to explore which socio-economic, geographical and biological factors explain social media use in sub-Saharan African protected areas. We found that charismatic species richness did not explain social media usage. On the other hand, protected areas that were more accessible, had sparser vegetation, where human population density was higher, and that were located in wealthier countries, had higher social media use. Interestingly, protected areas with lower richness in non-charismatic species had more users. Overall, our results suggest that more factors than simply charismatic species might explain attractiveness of protected areas, and call for more in-depth content analysis of the posts. With African countries projected to develop further in the near-future, more social media data will become available, and could be used to inform protected area management and marketing.

Enabling comparative modeling of closely related genomes: example genus Brucella.

For many scientific applications, it is highly desirable to be able to compare metabolic models of closely related genomes. In this short report, we attempt to raise awareness to the fact that taking annotated genomes from public repositories and using them for metabolic model reconstructions is far from being trivial due to annotation inconsistencies. We are proposing a protocol for comparative analysis of metabolic models on closely related genomes, using fifteen strains of genus Brucella, which contains pathogens of both humans and livestock. This study lead to the identification and subsequent correction of inconsistent annotations in the SEED database, as well as the identification of 31 biochemical reactions that are common to Brucella, which are not originally identified by automated metabolic reconstructions. We are currently implementing this protocol for improving automated annotations within the SEED database and these improvements have been propagated into PATRIC, Model-SEED, KBase and RAST. This method is an enabling step for the future creation of consistent annotation systems and high-quality model reconstructions that will support in predicting accurate phenotypes such as pathogenicity, media requirements or type of respiration.