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.     

Exploring human–nature interactions in national parks with social media photographs and computer vision

Understanding the activities and preferences of visitors is crucial for managing protected areas and planning conservation strategies. Conservation culturomics promotes the use of user-generated online content in conservation science. Geotagged social media content is a unique source of in situ information on human presence and activities in nature. Photographs posted on social media platforms are a promising source of information, but analyzing large volumes of photographs manually remains laborious. We examined the application of state-of-the-art computer-vision methods to studying human–nature interactions. We used semantic clustering, scene classification, and object detection to automatically analyze photographs taken in Finnish national parks by domestic and international visitors. Our results showed that human–nature interactions can be extracted from user-generated photographs with computer vision. The different methods complemented each other by revealing broad visual themes related to level of the data set, landscape photogeneity, and human activities. Geotagged photographs revealed distinct regional profiles for national parks (e.g., preferences in landscapes and activities), which are potentially useful in park management. Photographic content differed between domestic and international visitors, which indicates differences in activities and preferences. Information extracted automatically from photographs can help identify preferences among diverse visitor groups, which can be used to create profiles of national parks for conservation marketing and to support conservation strategies that rely on public acceptance. The application of computer-vision methods to automatic content analysis of photographs should be explored further in conservation culturomics, particularly in combination with rich metadata available on social media platforms.     

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.     

Sulfamethoxazole removal from polluted water by immobilized photocatalysis

The photocatalytic activity of TiO₂ deposits (Degussa P25 and Millennium PC500) has been studied using sulfamethoxazole (SMX) as a model water pollutant and a UV fluorescent lamp as a light source (365 nm). Both catalysts have shown very similar properties in the photocatalytic degradation of SMX. Special attention has been given to the effect of the irradiation time, pH, and pollutant concentration. No mass-transfer limitations are observed. The degradation of SMX is accelerated at low concentration, and the photocatalytic degradation kinetics obey the Langmuir–Hinshelwood model, allowing the adsorption and apparent rate constants to be determined for both catalysts.     

Removal of acid yellow 36 using Box–Behnken designed photoelectro-Fenton: a study on removal mechanisms

Photoelectro-Fenton was applied for the removal of acid yellow 36 (AY36) from synthetic aqueous solution using iron electrodes. A Box–Behnken design was used for optimization of the effects of pH, H₂O₂ concentration, current density, and reaction time. Individual effects of these variables were more important than their interaction effects. The derived model was in good agreement with the experimental results. Total organic carbon was determined in solution and sludge in order to clarify the removal mechanism. Increase of H₂O₂ concentration and current density led to domination of oxidation and coagulation mechanisms, respectively. The effects of scavenging and inhibiting agents were also investigated: (1) presence of alcohols can reduce the efficiency through competition with dye for reaction with hydroxyl radicals; (2) anions (NO₃^?, HCO₃^?, and H₂PO₄^?) scavenged hydroxyl radicals and reduced decolorization of AY36.     

Analysis of dust distribution in silo during axial filling using computational fluid dynamics: Assessment on dust explosion likelihood

In this study, the dust distribution in a silo during axial filling was modelled using a commercial computational fluid dynamics (CFD) code. The work focused on the dust concentration distribution in the silo, for evaluating the likelihood of a dust explosion in the silo. The simulation was conducted using a combination of renormalized (RNG) k-epsilon and discrete phase models, with standard pressure interpolation and a second order upwind scheme. The predicted dust concentration distribution showed a good agreement with experimental data adopted from the literature. It was found that the dust concentration distribution was influenced by mean velocity and turbulence flow. The simulation results suggest that the cornstarch concentration inside the silo was always above the lower explosion limit (LEL), hence requiring a mitigating action or a control system to reduce the explosion risk.     

Bypassing the flush,creating new resources: analysing alternative sanitation futures in London

The flush toilet is an illogical form of sanitation from the point of view of water conservation, nutrient recovery and water pollution. Places such as London, with one of the oldest flushing toilet and sewer systems in the world, bears witness to the limits of its universal applicability through dwindling freshwater resources and polluted waterways. It is therefore important to develop new forms of sanitation infrastructure. An actor–network theory coevolution framework is used to explore and gain insights into the coevolution pathways for new types and paradigms of sanitation in London, where waterborne sanitation is currently prevalent. This approach shows that while flushing toilets are currently stable network configurations, there are coevolution pathways that would shift the system towards dry sanitation. The quantity of freshwater resources available for toilet flushing was the main actant cited for the development of these coevolution pathways.     

Inadvertent environmentalism and the action–value opportunity: reflections from studies at both ends of the generational spectrum

A recent turn towards a more contextually sensitive apprehension of the challenge of making everyday life less resource hungry has been partly underwritten by widespread evidence that the environmental values people commonly profess to hold do not often translate into correspondingly low impact actions. Yet sometimes the contexts of everyday life can also conspire to make people limit their consumption without ever explicitly connecting this to the environmental agenda. This paper considers this phenomenon with reference to UK studies from both ends of the generational spectrum. The first questioned how older people keep warm at home during winter and the second examined how young people get rid of no longer wanted possessions. Both found that, though the respondents involved were acting in certain ways that may be deemed comparatively low impact, they were hitherto relatively indifferent to the idea of characterising these actions as such. We outline three ways in which sustainability advocates might respond to the existence of such “inadvertent environmentalists” and consider how they might inspire studies that generate fresh intervention ideas instead of lingering on the dispiriting recognition that people do not often feel able to act for the environment.     

Tropical arboreal ant mosaics: innate attraction and imprinting determine nest site selection in dominant ants

The modalities of nest site selection have, until now, been a key factor missing in the understanding of the arboreal ant mosaic, the manipulation of which could be used to favour one ant species to the detriment of others in biological control. We compared two dominant African arboreal ants of economic importance, Tetramorium aculeatum (Myrmicinae) and Oecophylla longinoda (Formicinae). The two species differed in terms of innate attraction to nesting site plants, their hierarchies of attractiveness being nearly inverse. Winged females and workers were confronted with choice tests using four plant species. By using winged females and workers originating from one of the plants to be tested, we showed the existence of a familiarisation process which can supersede innate attraction in both species. We recorded the same effect in neonatal workers bred in the laboratory in contact with a tested plant for 25 days after emergence, while mature workers could not be conditioned. There is, therefore, early learning, with a sensitive period after which the influence of the environment ceases, suggesting that this is a true imprinting process. Choice tests using neonates produced from larvae and pupae bred in the laboratory in contact with the leaves of each tested plant permitted us to demonstrate the existence of pre-imaginal learning. Nest site selection therefore depends on innate selective attraction and on environmental factors whose effect begins at the larval stage. There is, therefore, the potential to “control” imprinting, allowing one ant species to be favoured to the detriment of others in monospecific tree crop plantations. Received: 25 May 1998 / Accepted after revision: 9 October 1998     

Depressional runoff cascade networks of the Des Moines Lobe of Iowa

In this study, we develop a general mathematical framework and algorithm for routing cumulative precipitation excess through depressional fill–spill cascade networks in a landscape using only information about depression morphology, local contributing areas, and potential overland flow pathways. The framework also allows for the classification of depressions according to their landscape position within a network, and calculation of precipitation- and non-precipitation-dependent network properties, including measures of network complexity and runoff connectivity. To demonstrate its use, we applied our framework to the 167,287 drained depressions of the Des Moines Lobe of Iowa, a sub-region of the Prairie Pothole Region of North America, over a large range of historically observed precipitation amounts for scenarios both neglecting and incorporating infiltration in runoff generation. Our results show that 85.3% of depressions in this region form 18,851 unique depressional runoff cascade networks, with the remainder being disjunct features. Most of the properties of the region's networks appear to conform to either a truncated power-law or lognormal distribution. For a given rainfall amount, surface runoff connectivity between depressions within networks, and between networks and off-network areas, is controlled primarily by available aggregate depressional volumetric storage and contributing area, and to a lesser degree, network complexity.