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.     

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.     

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

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

X-ray absorption spectroscopy studies of reactions of technetium, uranium and neptunium with mackinawite

Technetium, uranium and neptunium may all occur in the environment in more than one oxidation state (IV or VII, IV or VI and IV or V respectively). The surface of mackinawite, the first-formed iron sulfide phase in anoxic conditions, can promote redox changes so a series of laboratory experiments were carried out to explore the interactions of Tc, U and Np with this mineral. The products of reaction were characterised using X-ray absorption spectroscopy. Technetium, added as TcO4(-), is reduced to oxidation state IV and forms a TcS(2)-like species. On oxidation of the mackinawite in air to form goethite, Tc remains in oxidation state IV but in an oxide, rather than a sulfide environment. At low concentrations, uranium forms uranyl surface complexes on oxidised regions of the mackinawite surface but at higher concentrations, the uranium promotes surface oxidation and forms a mixed oxidation state oxide phase. Neptunium is reduced to oxidation IV and forms a surface complex with surface sulfide ions. The remainder of the Np coordination sphere is filled with water molecules or hydroxide ions.     

和平发展与和平共存——中国的气候政策和亚太合作伙伴关系

《亚太清洁发展与气候新伙伴计划》（以下简称《新伙伴计划》）是国际气候政策中相对较新的一项机制。这项新伙伴计划由一些重要的国家参与组成,旨在应对一些严峻的长期挑战,其中包括人类活动引起的气候变化。参加该计划的伙伴——澳大利亚、中国、印度、日本、韩国和美国,以及最近加入的加拿大一起拥有世界一半以上的人口和超过50％的国内生产总值,其能源消耗和温室气体排放总量也占全球的一半以上。因此,这样一个“温室气体排放国的联合体”是国际气候政策中必不可少的要素。对中国来说,伙伴计划也是“和平发展”战略中气候政策的一个重要部分。     

Carbon dioxide emissions from spring ploughing of grassland in Ireland

Grassland re-seeding or land-use change requires ploughing, which may enhance carbon dioxide (CO₂) emissions from soil. This study observed the short to intermediate-term (37 days) effects of ploughing on CO₂ emissions from poorly drained grassland using automated soil respiration chambers. Immediately after ploughing, a brief peak in CO₂ emissions from soil occurred with a maximum observed flux of 6.91 g CO₂ m⁻² h⁻¹. Contradictory to other reported results, ecosystem respiration after ploughing was lower on the ploughed than on the grass site. After including estimates of photosynthesis in the analysis, ploughing led to significantly higher net CO₂ emissions than from grassland. The main mechanism of C loss during ploughing was most likely due to a reduction in gross primary production rather than enhanced soil respiration.     

Honeybees learn floral odors while receiving nectar from foragers within the hive

Recent studies showed that nectar odors brought back by honeybee foragers can be learned associatively inside the hive. In the present study, we focused on the learning abilities of bees, which directly interact via trophallaxis with the incoming nectar foragers: the workers that perform nectar-receiving tasks inside the hive. Workers that have received food directly from foragers coming back from a feeder offering either unscented or scented sugar solution [phenylacetaldehyde (PHE) or nonanal diluted] were captured from two observational hives, and their olfactory memories were tested using the proboscis extension response paradigm. Bees that have received scented solution from incoming foragers showed significantly increased response frequencies for the corresponding solution odor in comparison with those that have received unscented solution. No differences in the response frequencies were found between food odors and colonies. The results indicate that first-order receivers learn via trophallaxis the association between the scent and the sugar solution transferred by incoming foragers. The implications of these results should be considered at three levels: the operational cohesion of bees involved in foraging-related tasks, the information propagation inside the hive related to the floral type exploited, and the putative effect of these memories on future preferences for resources.     

Seasonal and Regional Patterns in Performance for a Baltic Sea Drainage Basin Hydrologic Model

This study evaluates the ability of the Catchment SIMulation (CSIM) hydrologic model to describe seasonal and regional variations in river discharge over the entire Baltic Sea drainage basin (BSDB) based on 31 years of monthly simulation from 1970 through 2000. To date, the model has been successfully applied to simulate annual fluxes of water from the catchments draining into the Baltic Sea. Here, we consider spatiotemporal bias in the distribution of monthly modeling errors across the BSDB since it could potentially reduce the fidelity of predictions and negatively affect the design and implementation of land‐management strategies. Within the period considered, the CSIM model accurately reproduced the annual flows across the BSDB; however, it tended to underpredict the proportion of discharge during high‐flow periods (i.e., spring months) and overpredict during the summer low flow periods. While the general overpredictions during summer periods are spread across all the subbasins of the BSDB, the underprediction during spring periods is seen largely in the northern regions. By implementing a genetic algorithm calibration procedure and/or seasonal parameterization of subsurface water flows for a subset of the catchments modeled, we demonstrate that it is possible to improve the model performance albeit at the cost of increased parameterization and potential loss of parsimony.     

Earthworms and legumes control litter decomposition in a plant diversity gradient

The role of species and functional group diversity of primary producers for decomposers and decomposition processes is little understood. We made use of the "Jena Biodiversity Experiment" and tested the hypothesis that increasing plant species (1, 4, and 16 species) and functional group diversity (1, 2, 3, and 4 groups) beneficially affects decomposer density and activity and therefore the decomposition of plant litter material. Furthermore, by manipulating the densities of decomposers (earthworms and springtails) within the plant diversity gradient we investigated how the interactions between plant diversity and decomposer densities affect the decomposition of litter belonging to different plant functional groups (grasses, herbs, and legumes). Positive effects of increasing plant species or functional group diversity on earthworms (biomass and density) and microbial biomass were mainly due to the increased incidence of legumes with increasing diversity. Neither plant species diversity nor functional group diversity affected litter decomposition, However, litter decomposition varied with decomposer and plant functional group identity (of both living plants and plant litter). While springtail removal generally had little effect on decomposition, increased earthworm density accelerated the decomposition of nitrogen-rich legume litter, and this was more pronounced at higher plant diversity. The results suggest that earthworms (Lumbricus terrestris L.) and legumes function as keystone organisms for grassland decomposition processes and presumably contribute to the recorded increase in primary productivity with increasing plant diversity.     

Photosynthetic performance of giant clams, Tridacna maxima and T. squamosa, Red Sea

Two species of giant clams, Tridacna maxima and T. squamosa, coexist in the Red Sea, but exhibit distinctly different depth distributions: T. maxima mostly occurs in shallow waters (reef flat and edge), while T. squamosa may occur down to the lower fore-reef slope. Giant clams have been described as mixotrophic, capable of both filter-feeding and photosynthesis due to algal symbionts (zooxanthellae), therefore, observed depth preferences were investigated in relation to possible differences in autotrophy vs. heterotrophy. This study was conducted from April to June 2004, at the reef near the Marine Science Station, Aqaba, Gulf of Aqaba, Red Sea, and in May 2007, at a reef near Dahab, Sinai Peninsula, Egypt. In situ measurements using a submersible pulse amplitude modulated fluorometer (Diving PAM), revealed no significant differences in effective PSII quantum yield (ΔF/Fm′) and relative electron transport rates (ETR) between the two species; but rapid light curves (ETR vs. light, photosynthetically active irradiance, PAR) showed significant differences in maximum photosynthetic rates (ETR_max), with 20% higher values in T. maxima. Chamber incubations displayed higher net and gross oxygen production by T. maxima (88.0 and 120.3 μmol O₂ cm⁻² mantle area day⁻¹) than T. squamosa (56.7 and 84.8 μmol O₂ cm⁻² mantle area day⁻¹); even under shading conditions (simulated depth of 20 m) T. maxima still achieved 93% of the surface gross O₂ production, whereas T. squamosa reached only 44%. A correlation was found between ETR and net photosynthesis measured as oxygen production (T. maxima: R ² = 0.53; T. squamosa: R ² = 0.61). Calculated compensation depth (CD) (gross photosynthesis equals respiration) in T. maxima (16 m) matches the maximum depth of occurrence in this study (17 m). By contrast, the CD of T. squamosa (9 m) was much shallower than the maximum vertical range (42 m). Findings suggest T. maxima is a strict functional photoautotroph limited by light, whereas T. squamosa is a mixotroph whose photoautotrophic range is extended by heterotrophy. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.