首页 | 本学科首页   官方微博 | 高级检索  
相似文献
 共查询到4条相似文献,搜索用时 0 毫秒
1.
    
The recent growth of online big data offers opportunities for rapid and inexpensive measurement of public interest. Conservation culturomics is an emerging research area that uses online data to study human–nature relationships for conservation. Methods for conservation culturomics, though promising, are still being developed and refined. We considered the potential of Wikipedia, the online encyclopedia, as a resource for conservation culturomics and outlined methods for using Wikipedia data in conservation. Wikipedia's large size, widespread use, underlying data structure, and open access to both its content and usage analytics make it well suited to conservation culturomics research. Limitations of Wikipedia data include the lack of location information associated with some metadata and limited information on the motivations of many users. Seven methodological steps to consider when using Wikipedia data in conservation include metadata selection, temporality, taxonomy, language representation, Wikipedia geography, physical and biological geography, and comparative metrics. Each of these methodological decisions can affect measures of online interest. As a case study, we explored these themes by analyzing 757 million Wikipedia page views associated with the Wikipedia pages for 10,099 species of birds across 251 Wikipedia language editions. We found that Wikipedia data have the potential to generate insight for conservation and are particularly useful for quantifying patterns of public interest at large scales.  相似文献   

2.
    
Raptors are threatened by anthropogenic land modifications, but targeted quantitative assessment of these impacts is lacking. We conducted the first global quantitative evaluation of the impacts of human-modified land on raptors. We used eBird data from 2001 to 2020 on 425 raptor species and occupancy models to assess the impacts of human-modified land on raptor distribution. The mean spatiotemporal correlations of human settlement, cropland, and pasture with raptor occupancy probability were −0.048 (SE 0.031), −0.134 (0.032), and −0.145 (0.032), respectively. The mean sensitivity of raptor occupancy probability to settlement, cropland, and pasture was −5.760 (2.266), −3.128 (1.540), and −2.402 (1.551), respectively. The occupancy probability of raptors with a large body mass was more negatively correlated with cropland (phylogenetic generalized least squares regressions: slope = −0.052 [SE 0.022], t = −2.335, df = 1, 407, p = 0.020, λ = 0.006) and more positively correlated with pasture (slope = 0.047 [0.022], t = 2.118, df = 1, 407, p = 0.035, λ = 0.013). The occupancy probability of raptors with a more extensive range size was more positively correlated with cropland (slope = 0.002 [0.004], t = 0.399, df = 1, 407, p < 0.001, λ = 0.000). Raptors that prefer open habitats were more positively correlated with cropland (analysis of variance: F = 3.424, df = 2, p = 0.034, λ = 0.000) and pasture (F = 6.577, df = 2, p = 0.002, λ = 0.000). In Africa and South America, where raptor species are most abundant, raptor occupancy probability decreased over 20 years, most likely due to habitat fragmentation associated with human land modification. Although raptors with different ecological characteristics had different responses to human land modification, the impacts of settlement, cropland, and pasture on mean raptor occupancy probability were negative, regardless of space and time.  相似文献   

3.
    
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.  相似文献   

4.
    
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.  相似文献   

设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号