Towards ecological science for all by all

Imagine a global community of people who—as a matter of daily routine—observe wildlife, smell the air, discuss the weather and touch the world around them. As sensors, people have evolved the ability to assess environmental change and one might easily conceive an army of such ‘citizen scientists’ ready and willing to advance knowledge. Of course, it is not that straightforward. Although volunteers have supported science for decades, the concept of citizen science that has a more holistic consideration of methods, ethics, philosophy and social science is relatively new. We know citizen science can generate large, high-quality datasets, but as much as this volume of observations demonstrates the potential of citizen science, there are new challenges and opportunities, such as reconnecting people with nature. No matter the discipline, how best to incorporate citizen science into research in an efficient, cost-effective and ethical way that works for both contributing citizen, and professional scientist is still to be fully understood. Indeed, from the perspective of the social sciences, there is the need for citizen science to adapt to a society that demands science to be responsive to rapidly changing concerns. The Oxford English Dictionary defined citizen science in 2014 (OED, 2020), and combined two early definitions that emphasised (a) the responsibility of science to society (Irwin, 1995) and (b) the participatory role of people contributing observations or efforts to scientific endeavours (Bonney, 1996). More recently, Ceccaroni et al. (2017) attempted to reconcile these viewpoints to describe citizen science as work undertaken by civic educators and scientists together with citizen communities to advance science, foster a broad scientific mentality, and/or encourage democratic engagement, which allows society to deal rationally with complex modern problems (Eitzel et al., 2017). As of 12th December 2020, the citizen science hub SciStarter.com lists 1,358 active citizen science projects. Of these, 642 (47.3%) are listed under the topic ‘Ecology and Environment’. Indeed, there has been a rapid growth in such projects since the 1940s, and since 1990 there has been a 10% decadal increase (Pocock et al., 2017). This expansion in citizen science has precipitated a range of supporting infrastructure, including typologies (e.g. Danielsen et al., 2009; Wiggins & Crowston, 2011), best practice principles (e.g. ECSA, 2017) and frameworks for implementation (Chase & Levine, 2016; Resnik et al., 2015; Shirk et al., 2012). It is because of the growth in citizen science activities across the breadth of ecology and environmental studies, and increasing attention in the social sciences, that the BES launched an open call for papers to this Special Feature on citizen science across six of the BES journals in October 2019. In this Editorial, we discuss the papers and topics covered and conclude with a brief outlook on ongoing and future developments. This Special Feature comprises 19 papers, of which six are in Journal of Applied Ecology, five in People and Nature, four in Journal of Animal Ecology, two in Ecological Solutions and Evidence and one each in Journal of Ecology and Methods in Ecology and Evolution. Although wide ranging and varied, as a collection these articles go some way to addressing the two perspectives intended for this Special Feature; the contribution of citizen science to the advancement of ecological knowledge and the contribution of community-based perspectives to citizen science. Among these 19 papers are 16 that present original quantitative or qualitative research, a practice-based article (Bonnet et al., 2021), a perspective (Palmer et al., 2021) and a literature review (Winch et al., 2021). Seven papers address aspects of quality assurance and quality control (QA/QC). Trust is a critical factor for leveraging partnerships between citizen scientists, project coordinators and decision-makers (Freitag et al., 2016). However, although an increasing number of disciplines incorporate citizen science, there remains scepticism of the public as a trusted source of scientific information (Burgess et al., 2017; Tredick et al., 2017) and many projects struggle to meet decision-maker needs (Newman et al., 2016), who require data suitable for reliable inference. Thus, underpinning quality assurance and control is that it is the scientific mode and quality of information that matters, which in the context of citizen science means ensuring that any participation in the data collection by community members may be improved by the development of procedures and protocols to be followed before and during data collection (quality assurance). This is also extended to processes for improving data quality after data collection, or ‘at the back-end’ (quality control). The papers by Pernat et al. (2021) and El Bizri et al. (2021) are two such studies of quality assurance. Pernat et al. (2021) compared trained researcher (or ‘professional’) data to citizen science data on mosquito collections in the German ‘Mückenatlas’, while El Bizri et al. (2021) compared local peoples’ capacity to determine the reproductive status of female pacas (Cuniculus paca). Pernat et al. (2021) used data from seven years of surveillance to evaluate what kind of information each method of collection provides. They found that systematic monitoring was superior in terms of mapping diversity, but passive monitoring did a better job detecting invasive species. This suggests that citizens can often do better at detecting novel occurrences than systematic approaches. With a 17-year long dataset, El Bizri et al. (2021) found that indigenous knowledge was already highly accurate for pregnancy diagnosis (72.5% correct) in female pacas, which was even better after training (88.2% correct). Thus, depending on the circumstances, citizen science data may not only complement systematic monitoring but can be superior to it by providing novel insights emerging from extensive local or indigenous knowledge. This has been seen in citizen science before: for example, with Hanny's Voorwerp on the Galaxy Zoo project (Cardamone et al., 2009). It is through insights such as these that blur the boundaries between those who are considered citizen scientists or professionals when either may produce sufficiently high-quality data to inform wildlife management. However, such high rates of agreement as found by El Bizri et al. (2021) may only be seen in about 55% of similar studies on the accuracy of citizen science data (Aceves-Bueno et al., 2017). The term ‘extreme’ citizen science, which has emerged in recent years, was borne partly out of internet accessibility, which has led to the development of thousands of web-based or mobile applications to aid the citizen scientists in recording accurate observations. Extreme citizen science involves not only the crowdsourcing of data but also its analysis (see Haklay, 2013). The ‘From Practice’ paper by Bonnet et al. (2021) presents the Pl@ntNet platform, which was initiated in 2009 and as well as being a platform for participatory research, aggregating and disseminating observations, it allows the identification of plants by automatic visual recognition. Bonnet et al. (2021) describe how Pl@ntNet was disseminated to local communities in two different socioeconomic contexts; the Ramières Reserve (France) and the Lewa Conservatory (Kenya). For such a tool to be widely adopted, the key factors they identify reinforce existing research and include good communication (Constant & Roberts, 2017), data quality and validation, and recognition of participant expectations (Dickinson et al., 2012). In line with the concept of extreme citizen science, this includes adopting an open data policy and ensuring technologies are commensurate with local infrastructure. The paper by Petersen et al. (2021) provides a forensic study of biological recording in Norway and finds classic taxonomic biases towards traditionally recorded and charismatic fauna (e.g. birds), and a non-random skew of observations towards anthropogenic land-uses (‘road-side bias’), whether the focus be on any, rare or non-native species. Thus, the study by Petersen et al. (2021) provides useful context for the remaining three papers under the theme of quality control which consider how to account for different forms of bias generated through citizen science data related to site selection and observer retention over time (Dambly et al., 2021), artificial light conditions (Ditmer et al., 2021), and choice of methods in generating less biased SDMs (Steen et al., 2021). With long-term monitoring, there is a concern that frequent turnover of observers can affect accuracy (Dickinson et al., 2010). Dambly et al. (2021) develop a ‘virtual ecologist’ model to test whether and how opportunistic site selection and uneven observer retention over time affect monitoring of bat roosts. They showed that these issues can result in biased trends, affecting the reliability of monitoring projects. Their findings highlight the value of engaging and retaining citizen science observers, a standardised sampling design and the collection of metadata. However, from an interdisciplinary perspective, what is termed ‘bias’ in the natural sciences may be part of the rich social dimensions that shape the science in particular ways; and it is this social shaping of the citizen science that the papers submitted in to People and Nature are largely concerned with. In particular because they alert us to the human and social dimensions related to why the concerns of citizen science may be particularly compelling to observers, who we might like to engage for extended periods of time. Observer bias may also be influenced by environmental conditions. Ditmer et al. (2021) tested whether the incorporation of artificial light at night (ALAN) conditions influenced the detection of American black bears (Ursus americanus) by citizen scientists. Members of the public provided 1,315 observations of black bear across Minnesota, USA. Using an occupancy modelling framework, the authors found that when compared to other commonly used metrics of human footprint (e.g. housing density), artificial light conditions did the best job of accounting for spatial bias showing higher rates of detection with elevated illuminance. Thus, bear abundance may be substantially underestimated in more natural conditions. Such spatial bias in volunteer effort associated with urban infrastructure is well established (Geldmann et al., 2016; Tiago, Ceia-Hasse, et al., 2017), but the added interaction of artificial light improves our understanding of detection bias, and can be incorporated into occupancy models to improve estimation and predictions of organisms’ distributions and abundances. Further error may result from methodological choices made during the production of SDMs based upon presence-only data, which is a frequent characteristic of citizen science. Such data generate class imbalances where one class (e.g. absence data) is far more abundant than another (Robinson et al., 2018). Steen et al. (2021) compare three choices for mitigating class imbalance: spatial thinning, class balancing and majority-only thinning using eBird data for 102 species. Finding that there was no single best approach across all species, and the considerable differences in SDM performance, the authors recommend a series of factors to be considered on a case-by-case basis to guide how to thin or balance data. The involvement of volunteers in recording species observations is one of the most established forms of citizen science (although it may not always have been referred to as such). For example, The Audubon Society sponsored Christmas Bird Count began in 1900 (Bock & Root, 1981) and the UK Butterfly Monitoring Scheme in 1976 (Pollard & Yates, 1994), closely followed by American and European equivalents. Such is the popularity of ornithology in particular, that bird data dominate biodiversity occurrence data on the global biodiversity information facility (Troudet et al., 2017), thanks in part, to the highly successfully citizen science platform eBird (Sullivan et al., 2009). When combined with conventional monitoring, citizen science data have broadened the opportunity for temporal ecological studies, for the study of and temporal It has been to improve our understanding of the of et al., and plants et al., in to a changing and of the of invasive species et al., 2011), and et al., 2017) and (Robinson et al., In the absence of citizen science data, many studies may be to species or a more or only a of an or an In this collection, the of eBird data by and (2021) has the between and bird to be across the than on a of the and across a broad spatial The of citizen science data has also to be across a number of species while studies have been in the number of species they have been to citizen science data from and (2021) to identify temporal of American The authors used the data from this to develop occupancy which model differences in the recording and detection The recent development and expansion of modelling such as these have a in the of citizen science data. The combined of citizen science data from or and in the that can be For example, et al. (2021) data on occurrence from abundance from the Bird and from to the between and species This study how citizen science projects may also more than the observations of a by observers to data on such as number of and such as new for citizen scientists to and conventional data (e.g. species in a more and systematic This is because can be in on and over more defined et al. (2021) such data in a study of and and at across data used to species occupancy models and that the of was and associated with with the on the data to develop a model of the authors also that are to in urban With policy and this study demonstrates how and of citizen science data through the of can to for natural (see also et al., to species and it is that et al. a approach to study the in of in of the across citizen science is for from across of the data from in with high human and with a higher for and et al., a that on from a much more study & In citizen science to monitoring of species and through the and of technologies and people who these over a spatial and temporal in to a The broadened spatial and temporal range of data collection also has the potential for for new insights and and that potential for citizen science to a role in long-term environmental change at spatial and temporal through and monitoring is not only for science and scientists but it is also for and environmental because it provides the basis for and et al., et al., successfully environmental change at long-term efforts to change from et al., 2010). science has been as a to this its potential to generate data at spatial and temporal not by conventional means (Pocock et al., et al., 2016). a factor to long-term monitoring is the of which may be by the of effort et al., It be to monitoring to volunteer as well as ecological and be the of monitoring to for example, bird This is an by et al. who 16 years of monitoring data to different of monitoring They that than the number of detection be most by but that in long-term might be by the number of but retaining the spatial this may the ability of a monitoring to early particularly in rare or species. Thus, social traditionally seen as aspects to be scientific research, aspects that inform both the of the data collection and the of the science is also an approach that and communities at the of the scientific et al. (2021) engage more than over a of seven years to the of upon from natural are found between and the five For example, are by and but the upon other is The authors the potential of monitoring being by which may have the of contributing to through an of and the role of biodiversity for et al. (2021) the social and of Indeed, the of any monitoring is a that be made by a wide of under the of social scientists et al., The two papers long-term monitoring in this Special Feature et al., et al., 2021) to this a approach but for a new monitoring by of information is the or for it to be what are the of the decision-makers that the more they to about of which is the focus of the selection of The of the term citizen science suggests that by including citizens or or in addressing scientific that it is by its a social or a The of papers focus on the of what to part in a citizen science This is a critical that be citizen science is to the of generating information at an spatial and temporal as well as et al., 2016). citizen science research has only to consider what to part and (e.g. et al., 2016), and the to which citizen science might public understanding of science or change et al., 2016). et al. (2021) a contribution to this of research with a systematic review of more than papers volunteer a of for they that the most factor is to design projects that to the of the More they go on to compare and the and of those volunteers who are a part of the to projects being by They do this to identify how such might projects to more to The community is and more in and than the conventional communities in environmental these may the of including citizen science and the volunteer However, as they and other authors the of volunteers are often and scientific are only to be a for a of to the as well as the and of may us better both why some are well in citizen science, and why some spatial including and still way to address such be to projects with to and et al., 2016). and (2021) how the Bird has been in to three that on different communication and as a the was to and communication to that the participant was more and that the of the better disseminated to a and more community of that this has been as an factor for long-term in citizen science et al., 2016; et al., However, efforts to community in the of the the authors the of and the as largely one of the of good than in understanding what volunteers to in citizen science, one might also what the less or are for are et al., 2021). are many to what these may social through addressing environmental or providing active forms of and involvement in local or global of the in the Special on the related to the role of or et al. for example, change in in the mapping project and not related to and to be by to other this context for the often long of that is when relatively volunteers generate the of the information (e.g. et al., and that even participation may have of citizen science, and more widely environmental is the between a and a termed et al. (2021) a model of for citizen science et al., and it to the The three dimensions community and natural with seven across them. the analysis of et al. (2021) that the in all three dimensions and how the of an may change over with for findings also the in but for citizen science et al., 2017). However, the authors highlight that more research is to whether the of is a of social or and In recent years, there has been an increasing focus on citizen science, both in the of volunteers et al., the contribution and status of social although less the ethical of The the of the around environmental in scientific research, and the potential shaping of citizen science by other in this (e.g. et al., This in is being by an increasing public concern for et al., for example, in and a on ethical issues in citizen science & In this Special et al. (2021) the for in particular, for those citizen science projects that such as methods or The authors a and of UK to research and citizen science and three key (a) of citizen science, (b) assess the of and consider the of The paper out its perspective on the need for citizen science to engage with the social and frameworks that shape society as well as science. have been in the of citizen science, and we are its to or model where species science data are being used to study species and biodiversity change to a as well as the of both participant and the of which this Special Feature provides an towards growth and expansion of the with to engage more people from generate better and more research, into new topic and to more the participant a However, one of the of this Special Feature is that many of the and about citizen science have not been it for example, to include a of in citizen science, citizen science remains to be by the challenges that this may the natural different can citizen science across the broad temporal and spatial that these studies have and to what can citizen science in local or indigenous knowledge in the scientific our understanding of what science can do and how it can to our environmental this Special be considered a to the citizen science to of the of a citizen science project from to The of citizen science be considered and not all projects are suitable for public (Pocock et al., the design of projects consider many including procedures for and mitigating the spatial and temporal of data how that data be and how best to the are not new for monitoring et al., but in a on volunteer effort like citizen science, they on Indeed, the emerging knowledge the focus from data to the complex but world of the to understanding the at a to and papers new insights into participant and have of projects to increase to good there is potential for the public to a understanding of and in science Indeed, there are many to best practice the In the future of citizen science remains As an interdisciplinary scientific we are to how it and are in the of the approach in an ethical in each of the as well as for For ecological research, citizen science can from a local to and can focus not only on species occurrences but also on a wide range of as provides much to citizen science, as that to the capacity of citizen science to

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.