1
|
Correia RA, Ladle R, Jarić I, Malhado ACM, Mittermeier JC, Roll U, Soriano-Redondo A, Veríssimo D, Fink C, Hausmann A, Guedes-Santos J, Vardi R, Di Minin E. Digital data sources and methods for conservation culturomics. CONSERVATION BIOLOGY : THE JOURNAL OF THE SOCIETY FOR CONSERVATION BIOLOGY 2021; 35:398-411. [PMID: 33749027 DOI: 10.1111/cobi.13706] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 04/27/2020] [Accepted: 06/05/2020] [Indexed: 05/20/2023]
Abstract
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.
Collapse
Affiliation(s)
- Ricardo A Correia
- Department of Geosciences and Geography, Helsinki Lab of Interdisciplinary Conservation Science, University of Helsinki, Helsinki, 00014, Finland
- Helsinki Institute of Sustainability Science (HELSUS), University of Helsinki, Helsinki, 00014, Finland
- CESAM - Centre for Environmental and Marine Studies, University of Aveiro, Campus Universitário de Santiago, Aveiro, 3910-193, Portugal
- Institute of Biological and Health Sciences, Federal University of Alagoas, Maceió, 57072-900, Brazil
| | - Richard Ladle
- Institute of Biological and Health Sciences, Federal University of Alagoas, Maceió, 57072-900, Brazil
- CIBIO/InBio, Centro de Investigação em Biodiversidade e Recursos Genéticos, Laboratório Associado, Universidade do Porto, Porto, 4485-661, Portugal
| | - Ivan Jarić
- Biology Centre of the Czech Academy of Sciences, Institute of Hydrobiology, České Budějovice, 37005, Czech Republic
- Department of Ecosystem Biology, Faculty of Science, University of South Bohemia, České Budějovice, 37005, Czech Republic
| | - Ana C M Malhado
- Institute of Biological and Health Sciences, Federal University of Alagoas, Maceió, 57072-900, Brazil
| | - John C Mittermeier
- School of Geography and the Environment, University of Oxford, Oxford, OX1 3QY, U.K
| | - Uri Roll
- Mitrani Department of Desert Ecology, The Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Midreshet Ben-Gurion, 8499000, Israel
| | - Andrea Soriano-Redondo
- CIBIO/InBio, Centro de Investigação em Biodiversidade e Recursos Genéticos, Laboratório Associado, Universidade do Porto, Porto, 4485-661, Portugal
- CIBIO/InBio, Centro de Investigação em Biodiversidade e Recursos Genéticos, Laboratório Associado, Instituto Superior de Agronomia, Universidade de Lisboa, Lisboa, 1349-017, Portugal
| | - Diogo Veríssimo
- Department of Zoology, University of Oxford, Oxford, OX1 3SZ, U.K
- Oxford Martin School, University of Oxford, Oxford, OX1 3BD, U.K
- San Diego Zoo Institute for Conservation Research, Escondido, CA, 92027, U.S.A
| | - Christoph Fink
- Department of Geosciences and Geography, Helsinki Lab of Interdisciplinary Conservation Science, University of Helsinki, Helsinki, 00014, Finland
- Helsinki Institute of Sustainability Science (HELSUS), University of Helsinki, Helsinki, 00014, Finland
| | - Anna Hausmann
- Department of Geosciences and Geography, Helsinki Lab of Interdisciplinary Conservation Science, University of Helsinki, Helsinki, 00014, Finland
- Helsinki Institute of Sustainability Science (HELSUS), University of Helsinki, Helsinki, 00014, Finland
| | - Jhonatan Guedes-Santos
- Institute of Biological and Health Sciences, Federal University of Alagoas, Maceió, 57072-900, Brazil
| | - Reut Vardi
- The Albert Katz International School for Desert Studies, The Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Midreshet Ben-GurionDurban, 8499000, Israel
| | - Enrico Di Minin
- Department of Geosciences and Geography, Helsinki Lab of Interdisciplinary Conservation Science, University of Helsinki, Helsinki, 00014, Finland
- Helsinki Institute of Sustainability Science (HELSUS), University of Helsinki, Helsinki, 00014, Finland
- School of Life Sciences, University of KwaZulu-Natal, Durban, 4041, South Africa
| |
Collapse
|
2
|
Abstract
Previous studies have shown that it is possible to detect macroscopic patterns of cultural change over periods of centuries by analyzing large textual time series, specifically digitized books. This method promises to empower scholars with a quantitative and data-driven tool to study culture and society, but its power has been limited by the use of data from books and simple analytics based essentially on word counts. This study addresses these problems by assembling a vast corpus of regional newspapers from the United Kingdom, incorporating very fine-grained geographical and temporal information that is not available for books. The corpus spans 150 years and is formed by millions of articles, representing 14% of all British regional outlets of the period. Simple content analysis of this corpus allowed us to detect specific events, like wars, epidemics, coronations, or conclaves, with high accuracy, whereas the use of more refined techniques from artificial intelligence enabled us to move beyond counting words by detecting references to named entities. These techniques allowed us to observe both a systematic underrepresentation and a steady increase of women in the news during the 20th century and the change of geographic focus for various concepts. We also estimate the dates when electricity overtook steam and trains overtook horses as a means of transportation, both around the year 1900, along with observing other cultural transitions. We believe that these data-driven approaches can complement the traditional method of close reading in detecting trends of continuity and change in historical corpora.
Collapse
|