Crowdsourcing the identification of organisms: A case-study of iSpot

Citizen science is a vital partnership for invasive alien species management and research

Invasive alien species (IAS) adversely impact biodiversity, ecosystem functions, and socio-economics. Citizen science can be an effective tool for IAS surveillance, management, and research, providing large datasets over wide spatial extents and long time periods, with public participants generating knowledge that supports action. We demonstrate how citizen science has contributed knowledge across the biological invasion process, especially for early detection and distribution mapping. However, we recommend that citizen science could be used more for assessing impacts and evaluating the success of IAS management. Citizen science does have limitations, and we explore solutions to two key challenges: ensuring data accuracy and dealing with uneven spatial coverage of potential recorders (which limits the dataset's "fit for purpose"). Greater co-development of citizen science with public stakeholders will help us better realize its potential across the biological invasion process and across ecosystems globally while meeting the needs of participants, local communities, scientists, and decision-makers.

Inequality persists in a large citizen science programme despite increased participation through ICT innovations

Biological recording is a prominent and widely practised form of citizen science, but few studies explore long-term demographic trends in participation and knowledge production. We studied long-term demographic trends of age and gender of participants reporting to a large online citizen science multi-taxon biodiversity platform ( www.artportalen.se ). Adoption by user communities and continually developing Information and Communications Technologies (ICTs) greatly increased the number of participants reporting data, but profound long-term imbalances in gender contribution across species groups persisted over time. Reporters identifying as male dominated in numbers, spent more days in the field reporting and reported more species on each field day. Moreover, an age imbalance towards older participants amplified over time. As the first long-term study of citizen participation by age and gender, our results show that it is important for citizen science project developers to account for cultural and social developments that might exclude participants, and to engage with underrepresented and younger participants. This could facilitate the breadth of engagement and learning across a larger societal landscape, ensure project longevity and biodiversity data representation (e.g. mitigate gender bias influence on the number of reports of different species groups).

Attitudes Toward Engagement in Citizen Science Increase Self-Related, Ecology-Related, and Motivation-Related Outcomes in an Urban Wildlife Project

Citizen science projects are crucial for engaging citizens in conservation efforts. Although attitudes toward engagement in citizen science were mostly considered an outcome of citizen science participation, citizens may also have a certain attitude toward engagement in citizen science when starting with a citizen science project. Moreover, there is a lack of citizen science studies that consider changes over longer periods of time. Therefore, in this research, we present two-wave data from four field studies of a citizen science project about urban wildlife ecology using cross-lagged panel analyses. We investigated the influence of attitudes toward engagement in citizen science on self-related, ecology-related, and motivation-related outcomes. We found that positive attitudes toward engagement in citizen science at the beginning of the project had positive influences on the participants' psychological ownership and pride in their participation, their attitudes toward and enthusiasm about wildlife, and their internal and external motivation 2 months later. We discuss the implications for citizen science research and practice.

Deep learning increases the availability of organism photographs taken by citizens in citizen science programs

Citizen science programs using organism photographs have become popular, but there are two problems related to photographs. One problem is the low quality of photographs. It is laborious to identify species in photographs taken outdoors because they are out of focus, partially invisible, or under different lighting conditions. The other is difficulty for non-experts to identify species. Organisms usually have interspecific similarity and intraspecific variation, which hinder species identification by non-experts. Deep learning solves these problems and increases the availability of organism photographs. We trained a deep convolutional neural network, Xception, to identify bee species using various quality of bee photographs that were taken by citizens. These bees belonged to two honey bee species and 10 bumble bee species with interspecific similarity and intraspecific variation. We investigated the accuracy of species identification by biologists and deep learning. The accuracy of species identification by Xception (83.4%) was much higher than that of biologists (53.7%). When we grouped bee photographs by different colors resulting from intraspecific variation in addition to species, the accuracy of species identification by Xception increased to 84.7%. The collaboration with deep learning and experts will increase the reliability of species identification and their use for scientific researches.

Chimpanzee identification and social network construction through an online citizen science platform

Citizen science has grown rapidly in popularity in recent years due to its potential to educate and engage the public while providing a means to address a myriad of scientific questions. However, the rise in popularity of citizen science has also been accompanied by concerns about the quality of data emerging from citizen science research projects. We assessed data quality in the online citizen scientist platform Chimp&See, which hosts camera trap videos of chimpanzees (Pan troglodytes) and other species across Equatorial Africa. In particular, we compared detection and identification of individual chimpanzees by citizen scientists with that of experts with years of experience studying those chimpanzees. We found that citizen scientists typically detected the same number of individual chimpanzees as experts, but assigned far fewer identifications (IDs) to those individuals. Those IDs assigned, however, were nearly always in agreement with the IDs provided by experts. We applied the data sets of citizen scientists and experts by constructing social networks from each. We found that both social networks were relatively robust and shared a similar structure, as well as having positively correlated individual network positions. Our findings demonstrate that, although citizen scientists produced a smaller data set based on fewer confirmed IDs, the data strongly reflect expert classifications and can be used for meaningful assessments of group structure and dynamics. This approach expands opportunities for social research and conservation monitoring in great apes and many other individually identifiable species.

An overview of the history, current contributions and future outlook of iNaturalist in Australia

Citizen science initiatives and the data they produce are increasingly common in ecology, conservation and biodiversity monitoring. Although the quality of citizen science data has historically been questioned, biases can be detected and corrected for, allowing these data to become comparable in quality to professionally collected data. Consequently, citizen science is increasingly being integrated with professional science, allowing the collection of data at unprecedented spatial and temporal scales. iNaturalist is one of the most popular biodiversity citizen science platforms globally, with more than 1.4 million users having contributed over 54 million observations. Australia is the top contributing nation in the southern hemisphere, and in the top four contributing nations globally, with over 1.6 million observations of over 36000 identified species contributed by almost 27000 users. Despite the platform’s success, there are few holistic syntheses of contributions to iNaturalist, especially for Australia. Here, we outline the history of iNaturalist from an Australian perspective, and summarise, taxonomically, temporally and spatially, Australian biodiversity data contributed to the platform. We conclude by discussing important future directions to maximise the usefulness of these data for ecological research, conservation and policy.

Automatic classification of older electronic texts into the Universal Decimal Classification–UDC

Purpose

The purpose of this study is to develop a model for automated classification of old digitised texts to the Universal Decimal Classification (UDC), using machine-learning methods.

Design/methodology/approach

The general research approach is inherent to design science research, in which the problem of UDC assignment of the old, digitised texts is addressed by developing a machine-learning classification model. A corpus of 70,000 scholarly texts, fully bibliographically processed by librarians, was used to train and test the model, which was used for classification of old texts on a corpus of 200,000 items. Human experts evaluated the performance of the model.

Findings

Results suggest that machine-learning models can correctly assign the UDC at some level for almost any scholarly text. Furthermore, the model can be recommended for the UDC assignment of older texts. Ten librarians corroborated this on 150 randomly selected texts.

Research limitations/implications

The main limitations of this study were unavailability of labelled older texts and the limited availability of librarians.

Practical implications

The classification model can provide a recommendation to the librarians during their classification work; furthermore, it can be implemented as an add-on to full-text search in the library databases.

Social implications

The proposed methodology supports librarians by recommending UDC classifiers, thus saving time in their daily work. By automatically classifying older texts, digital libraries can provide a better user experience by enabling structured searches. These contribute to making knowledge more widely available and useable.

Originality/value

These findings contribute to the field of automated classification of bibliographical information with the usage of full texts, especially in cases in which the texts are old, unstructured and in which archaic language and vocabulary are used.

Estimating possible bumblebee range shifts in response to climate and land cover changes

Wild bee decline has been reported worldwide. Some bumblebee species (Bombus spp.) have declined in Europe and North America, and their ranges have shrunk due to climate and land cover changes. In countries with limited historical and current occurrence data, it is often difficult to investigate bumblebee range shifts. Here we estimated the past/present distributions of six major bumblebee species in Japan with species distribution modeling using current occurrence data and past/present climate and land cover data. The differences identified between estimated past and present distributions indicate possible range shifts. The estimated ranges of B. diversus, B. hypocrita, B. ignitus, B. honshuensis, and B. beaticola shrank over the past 26 years, but that of B. ardens expanded. The lower altitudinal limits of the estimated ranges became higher as temperature increased. When focusing on the effects of land cover change, the estimated range of B. diversus slightly shrank due to an increase in forest area. Such increase in forest area may result from the abandonment of agricultural lands and the extension of the rotation time of planted coniferous forests and secondary forests. Managing old planted coniferous forests and secondary forests will be key to bumblebee conservation for adaptation to climate change.

AI Naturalists Might Hold the Key to Unlocking Biodiversity Data in Social Media Imagery

The increasing availability of digital images, coupled with sophisticated artificial intelligence (AI) techniques for image classification, presents an exciting opportunity for biodiversity researchers to create new datasets of species observations. We investigated whether an AI plant species classifier could extract previously unexploited biodiversity data from social media photos (Flickr). We found over 60,000 geolocated images tagged with the keyword "flower" across an urban and rural location in the UK and classified these using AI, reviewing these identifications and assessing the representativeness of images. Images were predominantly biodiversity focused, showing single species. Non-native garden plants dominated, particularly in the urban setting. The AI classifier performed best when photos were focused on single native species in wild situations but also performed well at higher taxonomic levels (genus and family), even when images substantially deviated from this. We present a checklist of questions that should be considered when undertaking a similar analysis.

Community science participants gain environmental awareness and contribute high quality data but improvements are needed: insights from Bumble Bee Watch

Bumble Bee Watch is a community science program where participants submit photos of bumble bees from across Canada and the United States for expert verification. The data can be used to help better understand bumble bee biology and aid in their conservation. Yet for community science programs like this to be successful and sustainable, it is important to understand the participant demographics, what motivates them, and the outcomes of their participation, as well as areas that are working well or could be improved. It is also important to understand who verifies the submissions, who uses the data and their views on the program. Of the surveyed users, most participate to contribute to scientific data collection (88%), because of a worry about bees and a desire to help save them (80%), to learn more about species in their property (63%) or region (56%), and because of a personal interest (59%). About 77% report increased awareness of species diversity, while 84% report improvement in their identification skills. We found that 81% had at least one college or university degree. There were more respondents from suburban and rural areas than urban areas, but area did not affect numbers of submissions. While half were between 45 and 64 years of age, age did not influence motivation or number of submissions. Respondents were happy with the program, particularly the website resources, the contribution to knowledge and conservation efforts, the educational values, and the ability to get identifications. Areas for improvement included app and website functionality, faster and more detailed feedback, localized resources, and more communication. Most respondents participate rarely and have submitted fewer than ten records, although about five percent are super users who participate often and submit more than fifty records. Suggested improvements to the program may increase this participation rate. Indeed, increased recruitment and retention of users in general is important, and advertising should promote the outcomes of participation. Fifteen experts responded to a separate survey and were favorable of the program although there were suggestions on how to improve the verification process and the quality of the submitted data. Suggested research questions that could be asked or answered from the data included filling knowledge gaps (species diversity, ranges, habitat, phenology, floral associations, etc.), supporting species status assessments, effecting policy and legislation, encouraging habitat restoration and management efforts, and guiding further research. However, only about half have used data from the project to date. Further promotion of Bumble Bee Watch and community science programs in general should occur amongst academia, conservationists, policy makers, and the general public. This would help to increase the number and scope of submissions, knowledge of these species, interest in conserving them, and the overall program impact.

Assessing the emergence of pro-biodiversity practices in citizen scientists of a backyard butterfly survey

By monitoring biodiversity through citizen science programs, volunteers help scientists gather data at unprecedented temporal and geographical scales, and increase their knowledge and awareness of the surrounding biodiversity. While scientific outcomes of such programs may in the long run improve the state of biodiversity by informing environmental policies, direct benefits to biodiversity could arise locally if such experience of nature leads to biodiversity-friendly behaviors in volunteers. However, whether engagement into nature-based CS programs promotes individual behavioral changes remains poorly known. Here, we explored whether sustained participation in a nature-based citizen science program, called the French Butterfly citizen science project, is associated with changes in individual gardening practices. Specifically, using information provided by volunteers (n = 2362, from 2006 to 2013), we quantified gardening practices that directly affect butterflies, through two different indices: provision of nectar resources, and pesticide use. We found quantitative evidence that individual gardening practices shifted with multi-year participation, towards increased provision of nectar resources and decreased use of pesticides. However, the reduction in pesticide use was weakened if the backyard was used to grow fruits or vegetables. Other variables such as the size of the backyard affected gardening practices. This study reveals that participation in a nature-based citizen science program can prompt biodiversity-friendly behaviors, and highlights citizen science not only as a way to collect ecologically sound data but also as a direct conservation tool. Yet, future interdisciplinary research remains critical to overcome factors limiting firm adoption of pro-biodiversity behaviors.

The Global Museum: natural history collections and the future of evolutionary science and public education

Natural history museums are unique spaces for interdisciplinary research and educational innovation. Through extensive exhibits and public programming and by hosting rich communities of amateurs, students, and researchers at all stages of their careers, they can provide a place-based window to focus on integration of science and discovery, as well as a locus for community engagement. At the same time, like a synthesis radio telescope, when joined together through emerging digital resources, the global community of museums (the ‘Global Museum’) is more than the sum of its parts, allowing insights and answers to diverse biological, environmental, and societal questions at the global scale, across eons of time, and spanning vast diversity across the Tree of Life. We argue that, whereas natural history collections and museums began with a focus on describing the diversity and peculiarities of species on Earth, they are now increasingly leveraged in new ways that significantly expand their impact and relevance. These new directions include the possibility to ask new, often interdisciplinary questions in basic and applied science, such as in biomimetic design, and by contributing to solutions to climate change, global health and food security challenges. As institutions, they have long been incubators for cutting-edge research in biology while simultaneously providing core infrastructure for research on present and future societal needs. Here we explore how the intersection between pressing issues in environmental and human health and rapid technological innovation have reinforced the relevance of museum collections. We do this by providing examples as food for thought for both the broader academic community and museum scientists on the evolving role of museums. We also identify challenges to the realization of the full potential of natural history collections and the Global Museum to science and society and discuss the critical need to grow these collections. We then focus on mapping and modelling of museum data (including place-based approaches and discovery), and explore the main projects, platforms and databases enabling this growth. Finally, we aim to improve relevant protocols for the long-term storage of specimens and tissues, ensuring proper connection with tomorrow’s technologies and hence further increasing the relevance of natural history museums.

The CrowdWater game: A playful way to improve the accuracy of crowdsourced water level class data

Data quality control is important for any data collection program, especially in citizen science projects, where it is more likely that errors occur due to the human factor. Ideally, data quality control in citizen science projects is also crowdsourced so that it can handle large amounts of data. Here we present the CrowdWater game as a gamified method to check crowdsourced water level class data that are submitted by citizen scientists through the CrowdWater app. The app uses a virtual staff gauge approach, which means that a digital scale is added to the first picture taken at a site and this scale is used for water level class observations at different times. In the game, participants classify water levels based on the comparison of the new picture with the picture containing the virtual staff gauge. By March 2019, 153 people had played the CrowdWater game and 841 pictures were classified. The average water level for the game votes for the classified pictures was compared to the water level class submitted through the app to determine whether the game can improve the quality of the data submitted through the app. For about 70% of the classified pictures, the water level class was the same for the CrowdWater app and game. For a quarter of the classified pictures, there was disagreement between the value submitted through the app and the average game vote. Expert judgement suggests that for three quarters of these cases, the game based average value was correct. The initial results indicate that the CrowdWater game helps to identify erroneous water level class observations from the CrowdWater app and provides a useful approach for crowdsourced data quality control. This study thus demonstrates the potential of gamified approaches for data quality control in citizen science projects.

Improving big citizen science data: Moving beyond haphazard sampling

Citizen science is mainstream: millions of people contribute data to a growing array of citizen science projects annually, forming massive datasets that will drive research for years to come. Many citizen science projects implement a “leaderboard” framework, ranking the contributions based on number of records or species, encouraging further participation. But is every data point equally “valuable?” Citizen scientists collect data with distinct spatial and temporal biases, leading to unfortunate gaps and redundancies, which create statistical and informational problems for downstream analyses. Up to this point, the haphazard structure of the data has been seen as an unfortunate but unchangeable aspect of citizen science data. However, we argue here that this issue can actually be addressed: we provide a very simple, tractable framework that could be adapted by broadscale citizen science projects to allow citizen scientists to optimize the marginal value of their efforts, increasing the overall collective knowledge.

Citizen scientists collect data with distinct spatial and temporal biases, leading to unfortunate gaps and redundancies, and creating statistical and informational problems for downstream analyses. This Essay argues that by using a tractable framework which incentivizes looking, rather than finding, this issue can actually be addressed.

Evaluating the ability of citizen scientists to identify bumblebee (Bombus) species

Citizen science is an increasingly popular way of engaging volunteers in the collection of scientific data. Despite this, data quality remains a concern and there is little published evidence about the accuracy of records generated by citizen scientists. Here we compare data generated by two British citizen science projects, Blooms for Bees and BeeWatch, to determine the ability of volunteer recorders to identify bumblebee (Bombus) species. We assessed recorders' identification ability in two ways-as recorder accuracy (the proportion of expert-verified records correctly identified by recorders) and recorder success (the proportion of recorder-submitted identifications confirmed correct by verifiers). Recorder identification ability was low (<50% accuracy; <60% success), despite access to project specific bumblebee identification materials. Identification ability varied significantly depending on bumblebee species, with recorders most able to correctly identify species with distinct appearances. Blooms for Bees recorders (largely recruited from the gardening community) were markedly less able to identify bumblebees than BeeWatch recorders (largely individuals with a more specific interest in bumblebees). Within both projects, recorders demonstrated an improvement in identification ability over time. Here we demonstrate and quantify the essential role of expert verification within citizen science projects, and highlight where resources could be strengthened to improve recorder ability.

Monetary and Social Rewards for Crowdsourcing

Crowdsourcing of inventive activities is a particular form of crowdsourcing that helps firms to innovate by involving dispersed individuals to exploit “crowd wisdom”. In this context, the greater the number of contributions, the greater the possibility to gather extremely valuable ideas to produce innovative products and services. While monetary and social rewards can be an effective means to boost contributors’ extrinsic and intrinsic motivations to contribute, a theoretical understanding and empirical evidence of their effects are lacking. This paper focused on the crowdsourcing of inventive activities, initiated by listed companies worldwide, from 2007 to 2014. Our findings shed light on the influence of monetary and social rewards on the number of ideas collected. In particular, we analyzed the impact on the number of contributions brought about by monetary rewards and noted a positive influence related to its presence and also a negative effect related to the amount of the compensation. Moreover, we have demonstrated how the presence of a social cause is beneficial to the number of contributions. Consequently, we contribute to a scholarly understanding of the crowdsourcing phenomenon and we have provided guidance to managers seeking to initiate crowdsourcing campaigns.

Interaction design of community-driven environmental projects (CDEPs): A case study from the Anacostia Watershed

Water is becoming an increasingly precious resource across the world, but citizens can help ensure good-quality water by helping to manage their local watersheds. Local, place-based advocacy projects that are strongly grounded within their geographical area have the potential to inspire environmental change as citizens come together to collectively address environmental challenges in their own neighborhoods. However, less attention has been given to these small, intensely place-based local projects. Our research focuses on the interaction design of technology to support small, local, place-based "community-driven environmental projects" (CDEPs) for water management in the Anacostia River Watershed. Analysis of case study data collected over a 3-y period to identify the needs of CDEP members provides the basis for the interaction design of technology known as NatureNet to support CDEPs. From this research, six design insights for supporting CDEPs are suggested: (i) a shared common endeavor connected to the identity of the members, (ii) ways for newcomers and experienced participants to share a common space and function together effectively, (iii) support for different sources of knowledge, (iv) different modes of participation and ways for participants to express themselves, (v) different ways to express status through diverse opportunities, and (vi) support for porous and changing leadership roles. Taken together, the findings from our study suggest a preference for technology-lite, highly flexible software that supports the diverse needs of CDEP members.

A scoping review provided a framework for new ways of doing research through mobilizing collective intelligence

OBJECTIVES

New forms of research involving collective intelligence (CI) of diverse individuals mobilized through crowdsourcing is successfully emerging in various fields. This scoping review aimed to describe these methods across different fields and propose a framework for implementation.

STUDY DESIGN AND SETTING

We searched seven electronic databases for reports describing projects that had mobilized CI with crowdsourcing. We used content analysis to develop themes and categories of the methods.

RESULTS

We identified 145 reports. CI was mobilized to generate ideas, conduct evaluations, solve problems, and create intellectual outputs. Most projects (n = 110, 76%) were open to the public without restrictions on participants' expertise. Participants contributed to projects by independent contribution (i.e., no interaction with other participants) (n = 50, 34%), collaboration (n = 41, 28%), competitions (n = 33, 23%), and playing games (n = 16, 11%). In total, 61% of articles (n = 89) reported methods to evaluate participants' contribution and decision-making process: 43% used an independent panel of experts and 18% involved end users. We identified challenges in implementation and sustainability of CI and proposed solutions.

CONCLUSION

New research methods based on CI through crowdsourcing could transform clinical research. This framework facilitates the implementation of these methods.

Designing online species identification tools for biological recording: the impact on data quality and citizen science learning

In recent years, the number and scale of environmental citizen science programmes that involve lay people in scientific research have increased rapidly. Many of these initiatives are concerned with the recording and identification of species, processes which are increasingly mediated through digital interfaces. Here, we address the growing need to understand the particular role of digital identification tools, both in generating scientific data and in supporting learning by lay people engaged in citizen science activities pertaining to biological recording communities. Starting from two well-known identification tools, namely identification keys and field guides, this study focuses on the decision-making and quality of learning processes underlying species identification tasks, by comparing three digital interfaces designed to identify bumblebee species. The three interfaces varied with respect to whether species were directly compared or filtered by matching on visual features; and whether the order of filters was directed by the interface or a user-driven open choice. A concurrent mixed-methods approach was adopted to compare how these different interfaces affected the ability of participants to make correct and quick species identifications, and to better understand how participants learned through using these interfaces. We found that the accuracy of identification and quality of learning were dependent upon the interface type, the difficulty of the specimen on the image being identified and the interaction between interface type and ‘image difficulty’. Specifically, interfaces based on filtering outperformed those based on direct visual comparison across all metrics, and an open choice of filters led to higher accuracy than the interface that directed the filtering. Our results have direct implications for the design of online identification technologies for biological recording, irrespective of whether the goal is to collect higher quality citizen science data, or to support user learning and engagement in these communities of practice.

The Increasing Disconnection of Primary Biodiversity Data from Specimens: How Does It Happen and How to Handle It?

Primary biodiversity data represent the fundamental elements of any study in systematics and evolution. They are, however, no longer gathered as they used to be and the mass-production of observation-based (OB) occurrences is overthrowing the collection of specimen-based (SB) occurrences. Although this change in practice is a major upheaval with significant consequences in the study of biodiversity, it remains understudied and has not attracted yet the attention it deserves. Analyzing 536 million occurrences from the Global Biodiversity Information Facility (GBIF) mediated data, we show that this spectacular change affects the 24 eukaryote taxonomic classes we targeted: from 1970 to 2016 the proportion of occurrences marked as traceable to tangible material (i.e., SB occurrences) fell from 68% to 18%; moreover, most of those specimen based-occurrences cannot be readily traced back to a specimen because the necessary information is missing. Ethical, practical or legal reasons responsible for this shift are known, and this situation appears unlikely to be reversed. Still, we urge scholars to acknowledge this dramatic change, embrace it and actively deal with it. Specifically, we emphasize why SB occurrences must be gathered, as a warrant to allow both repeating evolutionary studies and conducting rich and diverse investigations. When impossible to secure, voucher specimens must be replaced with OB occurrences combined with ancillary data (e.g., pictures, recordings, samples, DNA sequences). Ancillary data are instrumental for the usefulness of biodiversity occurrences and we show that, despite improving technologies to collate them, they remain rarely shared. The consequences of such a change are not yet clear but we advocate collecting material evidence or ancillary data to ensure that primary biodiversity data collected lately do not partly become obsolete when doubtful.

Designing citizen science tools for learning: lessons learnt from the iterative development of nQuire

This paper reports on a 4-year research and development case study about the design of citizen science tools for inquiry learning. It details the process of iterative pedagogy-led design and evaluation of the nQuire toolkit, a set of web-based and mobile tools scaffolding the creation of online citizen science investigations. The design involved an expert review of inquiry learning and citizen science, combined with user experience studies involving more than 200 users. These have informed a concept that we have termed 'citizen inquiry', which engages members of the public alongside scientists in setting up, running, managing or contributing to citizen science projects with a main aim of learning about the scientific method through doing science by interaction with others. A design-based research (DBR) methodology was adopted for the iterative design and evaluation of citizen science tools. DBR was focused on the refinement of a central concept, 'citizen inquiry', by exploring how it can be instantiated in educational technologies and interventions. The empirical evaluation and iteration of technologies involved three design experiments with end users, user interviews, and insights from pedagogy and user experience experts. Evidence from the iterative development of nQuire led to the production of a set of interaction design principles that aim to guide the development of online, learning-centred, citizen science projects. Eight design guidelines are proposed: users as producers of knowledge, topics before tools, mobile affordances, scaffolds to the process of scientific inquiry, learning by doing as key message, being part of a community as key message, every visit brings a reward, and value users and their time.

Species identification by conservation practitioners using online images: accuracy and agreement between experts

Emerging technologies have led to an increase in species observations being recorded via digital images. Such visual records are easily shared, and are often uploaded to online communities when help is required to identify or validate species. Although this is common practice, little is known about the accuracy of species identification from such images. Using online images of newts that are native and non-native to the UK, this study asked holders of great crested newt (Triturus cristatus) licences (issued by UK authorities to permit surveying for this species) to sort these images into groups, and to assign species names to those groups. All of these experts identified the native species, but agreement among these participants was low, with some being cautious in committing to definitive identifications. Individuals’ accuracy was also independent of both their experience and self-assessed ability. Furthermore, mean accuracy was not uniform across species (69–96%). These findings demonstrate the difficulty of accurate identification of newts from a single image, and that expert judgements are variable, even within the same knowledgeable community. We suggest that identification decisions should be made on multiple images and verified by more than one expert, which could improve the reliability of species data.

Description of a new species of Lamellothyrea Krikken (Coleoptera, Scarabaeidae, Cetoniinae) from the iSimangaliso Wetland Park, KwaZulu-Natal (South Africa)

Recent data and material obtained from northern KwaZulu-Natal (South Africa) and Maputo Bay (Mozambique) have provided support for the description of a new species of the genus Lamellothyrea Krikken, 1980. The genus previously included only one species, L.descarpentriesi, with uncertain and poorly defined type locality, i.e. “Transvaal”. It is now evident that two different species are actually involved, L.descarpentriesi with currently known distribution limited to the coastal area north of Maputo, and L.isimangalisosp. n. with a known distribution range virtually restricted to the iSimangaliso Wetland Park, in north-eastern KwaZulu-Natal. The two species appear to be separated by a substantial discontinuity in southern Mozambique and can be easily separated on the basis of their clypeal structure, extent of white dorsal tomentum and shape of aedeagal parameres. Both species appear to be restricted to the coastal belt, with L.isimangaliso sp. n. occupying almost exclusively dune forest habitats. In this species, adult activity depends on rainfall and shows two peaks, one at the onset of summer and the second in autumn.

Citizen science provides a reliable and scalable tool to track disease-carrying mosquitoes

Recent outbreaks of Zika, chikungunya and dengue highlight the importance of better understanding the spread of disease-carrying mosquitoes across multiple spatio-temporal scales. Traditional surveillance tools are limited by jurisdictional boundaries and cost constraints. Here we show how a scalable citizen science system can solve this problem by combining citizen scientists' observations with expert validation and correcting for sampling effort. Our system provides accurate early warning information about the Asian tiger mosquito (Aedes albopictus) invasion in Spain, well beyond that available from traditional methods, and vital for public health services. It also provides estimates of tiger mosquito risk comparable to those from traditional methods but more directly related to the human-mosquito encounters that are relevant for epidemiological modelling and scalable enough to cover the entire country. These results illustrate how powerful public participation in science can be and suggest citizen science is positioned to revolutionize mosquito-borne disease surveillance worldwide.

Utilization of photographs taken by citizens for estimating bumblebee distributions

Citizen science is a powerful tool for collecting large volumes of observational data on various species. These data are used to estimate distributions using environmental factors with Species Distribution Models (SDM). However, if citizens are inexperienced in recognizing organisms, they may report different species as the subject species. Here we show nation-wide bumblebee distributions using photographs taken by citizens in our project, and estimated distributions for six bumblebee species using land use, climate, and altitude data with SDM. We identified species from photographic images, and took their locations from GPS data of photographs or the text in e-mails. When we compared our data with conventional data for specimens in the Global Biodiversity Information Facility (GBIF), we found that the volume and the number of species were larger, and the bias of spatial range was lower, than those of GBIF. Our estimated distributions were more consistent with bumblebee distributions reported in previous studies than with those of GBIF. Our method was effective for collecting distribution data, and estimating distributions with SDM. The estimated SDM allows us to predict the previous and future species distributions, and to develop conservation policies taking account of future city planning and/or global climate changes.

Changes in the composition and distribution of alien plants in South Africa: An update from the Southern African Plant Invaders Atlas

Background: Data on alien species status and occurrence are essential variables for the monitoring and reporting of biological invasions. The Southern African Plant Invaders Atlas (SAPIA) Project has, over the past 23 years, atlassed alien plants growing outside of cultivation. Objectives: To document changes in the alien plant taxa recorded in SAPIA, assess trends in invasive distributions and explore effects of management and regulations.Method: The numbers of alien plant taxa recorded were compared between May 2006 and May 2016, and changes in the extent of invasions at a quarter-degree squares (qds) scale were compared between 2000 and 2016. The effectiveness of regulations and interventions was assessed in terms of the relative change in the extent of invasions. Results: As of May 2016, SAPIA had records for 773 alien plant taxa, an increase of 172 since 2006. Between 2000 and 2016, the number of qds occupied by alien plants increased by ~50%, due both to ongoing sampling and to spread. Successful classical biological control programmes have reduced the rate of spread of some taxa and in a few cases have led to range contractions. However, other interventions had no detectable effect at a qds scale. Conclusions: South Africa has a growing number of invasive alien plant species across an increasing area. More taxa should be listed under national regulations, but ultimately more needs to be done to ensure that management is strategic and effective. SAPIA is a valuable tool for monitoring alien plant status and should be developed further so that invasions can be accurately tracked over time.

Patterns of contribution to citizen science biodiversity projects increase understanding of volunteers' recording behaviour

The often opportunistic nature of biological recording via citizen science leads to taxonomic, spatial and temporal biases which add uncertainty to biodiversity estimates. However, such biases may also give valuable insight into volunteers' recording behaviour. Using Greater London as a case-study we examined the composition of three citizen science datasets - from Greenspace Information for Greater London CIC, iSpot and iRecord - with respect to recorder contribution and spatial and taxonomic biases, i.e. when, where and what volunteers record. We found most volunteers contributed few records and were active for just one day. Each dataset had its own taxonomic and spatial signature suggesting that volunteers' personal recording preferences may attract them towards particular schemes. There were also patterns across datasets: species' abundance and ease of identification were positively associated with number of records, as was plant height. We found clear hotspots of recording activity, the 10 most popular sites containing open water. We note that biases are accrued as part of the recording process (e.g. species' detectability) as well as from volunteer preferences. An increased understanding of volunteer behaviour gained from analysing the composition of records could thus enhance the fit between volunteers' interests and the needs of scientific projects.

To have your citizen science cake and eat it? Delivering research and outreach through Open Air Laboratories (OPAL)

BACKGROUND

The vast array of citizen science projects which have blossomed over the last decade span a spectrum of objectives from research to outreach. While some focus primarily on the collection of rigorous scientific data and others are positioned towards the public engagement end of the gradient, the majority of initiatives attempt to balance the two. Although meeting multiple aims can be seen as a 'win-win' situation, it can also yield significant challenges as allocating resources to one element means that they may be diverted away from the other. Here we analyse one such programme which set out to find an effective equilibrium between these arguably polarised goals. Through the lens of the Open Air Laboratories (OPAL) programme we explore the inherent trade-offs encountered under four indicators derived from an independent citizen science evaluation framework. Assimilating experience from the OPAL network we investigate practical approaches taken to tackle arising tensions.

RESULTS

Working backwards from project delivery to design, we found the following elements to be important: ensuring outputs are fit for purpose, developing strong internal and external collaborations, building a sufficiently diverse partnership and considering target audiences. We combine these 'operational indicators' with four pre-existing 'outcome indicators' to create a model which can be used to shape the planning and delivery of a citizen science project.

CONCLUSIONS

Our findings suggest that whether the proverb in the title rings true will largely depend on the identification of challenges along the way and the ability to address these conflicts throughout the citizen science project.

Surveying the citizen science landscape: an exploration of the design, delivery and impact of citizen science through the lens of the Open Air Laboratories (OPAL) programme

BACKGROUND

This paper provides a short introduction to the topic of citizen science (CS) identifying the shift from the knowledge deficit model to more inclusive, participatory science. It acknowledges the benefits of new technology and the opportunities it brings for mass participation and data manipulation. It focuses on the increase in interest in CS in recent years and draws on experience gained from the Open Air Laboratories (OPAL) programme launched in England in 2007.

METHODS

The drivers and objectives for OPAL are presented together with background information on the partnership, methods and scales. The approaches used by researchers ranged from direct public participation in mass data collection through field surveys to research with minimal public engagement. The supporting services focused on education, particularly to support participants new to science, a media strategy and data services.

RESULTS

Examples from OPAL are used to illustrate the different approaches to the design and delivery of CS that have emerged over recent years and the breadth of opportunities for public participation the current landscape provides. Qualitative and quantitative data from OPAL are used as evidence of the impact of CS.

CONCLUSION

While OPAL was conceived ahead of the more recent formalisation of approaches to the design, delivery and analysis of CS projects and their impact, it nevertheless provides a range of examples against which to assess the various benefits and challenges emerging in this fast developing field.

Crowdsourcing Without a Crowd

We present an incremental Bayesian model that resolves key issues of crowd size and data quality for consensus labeling. We evaluate our method using data collected from a real-world citizen science program, B ee W atch , which invites members of the public in the United Kingdom to classify (label) photographs of bumblebees as one of 22 possible species. The biological recording domain poses two key and hitherto unaddressed challenges for consensus models of crowdsourcing: (1) the large number of potential species makes classification difficult, and (2) this is compounded by limited crowd availability, stemming from both the inherent difficulty of the task and the lack of relevant skills among the general public. We demonstrate that consensus labels can be reliably found in such circumstances with very small crowd sizes of around three to five users (i.e., through group sourcing). Our incremental Bayesian model, which minimizes crowd size by re-evaluating the quality of the consensus label following each species identification solicited from the crowd, is competitive with a Bayesian approach that uses a larger but fixed crowd size and outperforms majority voting. These results have important ecological applicability: biological recording programs such as B ee W atch can sustain themselves when resources such as taxonomic experts to confirm identifications by photo submitters are scarce (as is typically the case), and feedback can be provided to submitters in a timely fashion. More generally, our model provides benefits to any crowdsourced consensus labeling task where there is a cost (financial or otherwise) associated with soliciting a label.

A generalized approach for producing, quantifying, and validating citizen science data from wildlife images

Citizen science has the potential to expand the scope and scale of research in ecology and conservation, but many professional researchers remain skeptical of data produced by nonexperts. We devised an approach for producing accurate, reliable data from untrained, nonexpert volunteers. On the citizen science website www.snapshotserengeti.org, more than 28,000 volunteers classified 1.51 million images taken in a large-scale camera-trap survey in Serengeti National Park, Tanzania. Each image was circulated to, on average, 27 volunteers, and their classifications were aggregated using a simple plurality algorithm. We validated the aggregated answers against a data set of 3829 images verified by experts and calculated 3 certainty metrics-level of agreement among classifications (evenness), fraction of classifications supporting the aggregated answer (fraction support), and fraction of classifiers who reported "nothing here" for an image that was ultimately classified as containing an animal (fraction blank)-to measure confidence that an aggregated answer was correct. Overall, aggregated volunteer answers agreed with the expert-verified data on 98% of images, but accuracy differed by species commonness such that rare species had higher rates of false positives and false negatives. Easily calculated analysis of variance and post-hoc Tukey tests indicated that the certainty metrics were significant indicators of whether each image was correctly classified or classifiable. Thus, the certainty metrics can be used to identify images for expert review. Bootstrapping analyses further indicated that 90% of images were correctly classified with just 5 volunteers per image. Species classifications based on the plurality vote of multiple citizen scientists can provide a reliable foundation for large-scale monitoring of African wildlife.

Review of Afraustraloderes rassei Bouyer, 2012: description of its female and a new species of Pixodarus Fairmaire, 1887 (Coleoptera, Cerambycidae, Prioninae)

The original description of Afraustraloderes rassei Bouyer, 2012 included a female that is now recognized as a separate species belonging to the genus Pixodarus and here described as Pixodarus spiniscapus sp. n. The true female of Afraustraloderes rassei has also been obtained recently and is, therefore, here described. The synonymy of Pixodarus exasperatus with Pixodarus nyassae, proposed earlier by Santos Ferreira (1980), is here supported. Conversely, the earlier inclusion of Afraustraloderes rassei in the tribe Hopliderini is rejected, on the basis of a key set of characters established by Quentin and Villiers (1972, 1975). Afraustraloderes rassei appears to be restricted to the Cape Floral Region, exhibiting larval development in trunks and roots of dead Proteaceae plants. Conversely, Pixodarus spiniscapus has so far only been recorded in the eastern part of South Africa and appears to be associated with bushveld vegetation.

Focal Plant Observations as a Standardised Method for Pollinator Monitoring: Opportunities and Limitations for Mass Participation Citizen Science

BACKGROUND

Recently there has been increasing focus on monitoring pollinating insects, due to concerns about their declines, and interest in the role of volunteers in monitoring pollinators, particularly bumblebees, via citizen science.

METHODOLOGY / PRINCIPAL FINDINGS

The Big Bumblebee Discovery was a one-year citizen science project run by a partnership of EDF Energy, the British Science Association and the Centre for Ecology & Hydrology which sought to assess the influence of the landscape at multiple scales on the diversity and abundance of bumblebees. Timed counts of bumblebees (Bombus spp.; identified to six colour groups) visiting focal plants of lavender (Lavendula spp.) were carried out by about 13 000 primary school children (7-11 years old) from over 4000 schools across the UK. 3948 reports were received totalling 26 868 bumblebees. We found that while the wider landscape type had no significant effect on reported bumblebee abundance, the local proximity to flowers had a significant effect (fewer bumblebees where other flowers were reported to be >5m away from the focal plant). However, the rate of mis-identifcation, revealed by photographs uploaded by participants and a photo-based quiz, was high.

CONCLUSIONS / SIGNIFICANCE

Our citizen science results support recent research on the importance of local flocal resources on pollinator abundance. Timed counts of insects visiting a lure plant is potentially an effective approach for standardised pollinator monitoring, engaging a large number of participants with a simple protocol. However, the relatively high rate of mis-identifications (compared to reports from previous pollinator citizen science projects) highlights the importance of investing in resources to train volunteers. Also, to be a scientifically valid method for enquiry, citizen science data needs to be sufficiently high quality, so receiving supporting evidence (such as photographs) would allow this to be tested and for records to be verified.

Biodiversity data mining from Argus-eyed citizens: the first illegal introduction record of Lepomis macrochirus macrochirus Rafinesque, 1819 in Japan based on Twitter information

An apparent illegal introduction of Lepomismacrochirusmacrochirus from Yokohama City, Kanagawa Prefecture, Japan, is reported based on a juvenile specimen and a photograph of two adults collected on 14 June 2015 and deposited in the Kangawa Prefectural Museum of Natural History. The specimens and photographs were initially reported on the internet-based social networking site, Twitter. Two specimens of Carassiusauratus, including an aquarium form, were also reported at the same locality and date, suggesting that the illegal introductions originated from an aquarium release. Our report demonstrates an example of web data mining in the discipline of Citizen Science.

Digital technology and the conservation of nature

Digital technology is changing nature conservation in increasingly profound ways. We describe this impact and its significance through the concept of 'digital conservation', which we found to comprise five pivotal dimensions: data on nature, data on people, data integration and analysis, communication and experience, and participatory governance. Examining digital innovation in nature conservation and addressing how its development, implementation and diffusion may be steered, we warn against hypes, techno-fix thinking, good news narratives and unverified assumptions. We identify a need for rigorous evaluation, more comprehensive consideration of social exclusion, frameworks for regulation and increased multi-sector as well as multi-discipline awareness and cooperation. Along the way, digital technology may best be reconceptualised by conservationists from something that is either good or bad, to a dual-faced force in need of guidance.