Henrikki Tenkanen

Global protected area expansion is compromised by projected land-use and parochialism

Protected areas are one of the main tools for halting the continuing global biodiversity crisis caused by habitat loss, fragmentation and other anthropogenic pressures. According to the Aichi Biodiversity Target 11 adopted by the Convention on Biological Diversity, the protected area network should be expanded to at least 17% of the terrestrial world by 2020 (http://www.cbd.int/sp/targets). To maximize conservation outcomes, it is crucial to identify the best expansion areas. Here we show that there is a very high potential to increase protection of ecoregions and vertebrate species by expanding the protected area network, but also identify considerable risk of ineffective outcomes due to land-use change and uncoordinated actions between countries. We use distribution data for 24,757 terrestrial vertebrates assessed under the International Union for the Conservation of Nature (IUCN) ‘red list of threatened species’, and terrestrial ecoregions (827), modified by land-use models for the present and 2040, and introduce techniques for global and balanced spatial conservation prioritization. First, we show that with a coordinated global protected area network expansion to 17% of terrestrial land, average protection of species ranges and ecoregions could triple. Second, if projected land-use change by 2040 (ref. 11) takes place, it becomes infeasible to reach the currently possible protection levels, and over 1,000 threatened species would lose more than 50% of their present effective ranges worldwide. Third, we demonstrate a major efficiency gap between national and global conservation priorities. Strong evidence is shown that further biodiversity loss is unavoidable unless international action is quickly taken to balance land-use and biodiversity conservation. The approach used here can serve as a framework for repeatable and quantitative assessment of efficiency, gaps and expansion of the global protected area network globally, regionally and nationally, considering current and projected land-use pressures.

Prospects and challenges for social media data in conservation science

Social media data have been extensively used in numerous fields of science, but examples of their use in conservation science are still very limited. In this paper, we propose a framework on how social media data could be useful for conservation science and practice. We present the commonly used social media platforms and discuss how their content could be providing new data and information for conservation science. Based on this, we discuss how future work in conservation science and practice would benefit from social media data.

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.

Enhancing spatial accuracy of mobile phone data using multi-temporal dasymetric interpolation

ABSTRACT Novel digital data sources allow us to attain enhanced knowledge about locations and mobilities of people in space and time. Already a fast-growing body of literature demonstrates the applicability and feasibility of mobile phone-based data in social sciences for considering mobile devices as proxies for people. However, the implementation of such data imposes many theoretical and methodological challenges. One major issue is the uneven spatial resolution of mobile phone data due to the spatial configuration of mobile network base stations and its spatial interpolation. To date, different interpolation techniques are applied to transform mobile phone data into other spatial divisions. However, these do not consider the temporality and societal context that shapes the human presence and mobility in space and time. The paper aims, first, to contribute to mobile phone-based research by addressing the need to give more attention to the spatial interpolation of given data, and further by proposing a dasymetric interpolation approach to enhance the spatial accuracy of mobile phone data. Second, it contributes to population modelling research by combining spatial, temporal and volumetric dasymetric mapping and integrating it with mobile phone data. In doing so, the paper presents a generic conceptual framework of a multi-temporal function-based dasymetric (MFD) interpolation method for mobile phone data. Empirical results demonstrate how the proposed interpolation method can improve the spatial accuracy of both night-time and daytime population distributions derived from different mobile phone data sets by taking advantage of ancillary data sources. The proposed interpolation method can be applied for both location- and person-based research, and is a fruitful starting point for improving the spatial interpolation methods for mobile phone data. We share the implementation of our method in GitHub as open access Python code.

Machine learning for tracking illegal wildlife trade on social media

To the Editor — Illegal trade in wildlife is booming on e-commerce platforms1, the ‘dark web’2 and social media1,3. The ease of access and high number of users of social media make it a particularly concerning venue1,3,4. Wildlife dealers use social media to release photos and information about products to attract customers and to market their products to networks of contacts. We currently lack the tools for automated monitoring of high-volume data that are needed to investigate and prevent this illegal trade, but machine-learning algorithms offer a way forward. Operating within the broader field of artificial intelligence, the concept of machine learning refers to algorithms that learn from data without human guidance. Deep-learning algorithms5 are a family of these algorithms that are highly successful in classifying image contents and locating individual objects within them, and in processing natural language. Applying these techniques to social media data allows human behaviour to be investigated on an unprecedented scale. Yet these techniques and data sources are still rarely used to address drivers of the biodiversity crisis6. Many social media platforms provide an application programming interface that allows access to user-generated text, images and videos, as well as to accompanying metadata, such as where and when the content was uploaded, and connections between users. Processing such data manually is inefficient and time consuming, but machine-learning algorithms can be trained to filter this content to identify relevant information (see Fig. 1 for an example). These algorithms can be trained to detect which species or wildlife products, such as horns or scales, appear in an image or video, while also classifying their setting, such as a natural habitat or a marketplace. When processing video, algorithms can use audio clues, such as identifying bird species by their songs and calls, as well as interrogating the image stream. Natural language processing can be used to infer the meaning of a verbal description, for example, whether an animal or plant is for sale or observed in nature, and to classify the sentiment and preferences of social media users. To unlock this potential, social media platforms must be engaged to share their data and actively collaborate in the development of real-time monitoring tools that can be used to automatically identify content pertaining to the illegal wildlife trade, and to report this content to enforcers. Furthermore, machine-learning algorithms need humanverified training data. Such training datasets may be generated through crowd-sourcing initiatives, and collaborations between scientists and enforcers may further improve the algorithms’ performance. Together with advances in artificial intelligence that will refine the algorithms themselves, such efforts

A framework for investigating illegal wildlife trade on social media with machine learning

Abstract Article impact statement: Machine learning can be used to automatically monitor and assess illegal wildlife trade on social media platforms.