Half a billion eyes on the ground: citizen science contributes to research, policy and management of biological invasions in Europe

Invasive alien species (IAS) are a key driver of global biodiversity loss. Reducing their spread and impact is a target of the Sustainable Development Goals (SDG target 15.8) and of the EU IAS Regulation 1143/2014. The use of citizen science offers various benefits to alien species (AS) decision making and to society, since public participation in research and management boosts awareness, engagement and scientific literacy and can reduce conflict in IAS management. We report the results of a survey on AS citizen science initiatives within the framework of European Cooperation in Science and Technology (COST) Action Alien-CSI. We gathered metadata on 103 initiatives across 41 countries, excluding general biodiversity reporting portals, spanning from 2005 to 2020, offering the most comprehensive account of AS citizen science initiatives on the continent to date. We retrieved information on project scope, policy relevance, engagement methods, data capture, data quality and data management, methods and technologies applied and performance indicators such as the number of records coming from projects, the numbers of participants and publications. The 103 initiatives were unevenly Author-formatted, not peer-reviewed document posted on 01/02/2022. DOI:  https://doi.org/10.3897/arphapreprints.e81567


Introduction
The history of citizen science (CS), broadly defined as the practice of involving members of the public in scientific research (Wiggins and Crowston 2011), can be traced back centuries (Silvertown 2009), but over the course of recent decades, the field of CS has grown and transformed to reflect new attitudes and capabilities. Large and diverse audiences across the globe now contribute to initiatives carried out on scales ranging from short-term and local, to generational and international. The role of the citizen scientist is equally variable and as a result, the definition of CS has been subject to debate (Heigl et al. 2019;Haklay et al. 2021). In many environmental projects, citizen scientists merely collect and submit field observations to be analyzed by professional scientists (Bonney 1996); however, in this paper we also consider more in-depth involvement of citizen scientist, such as measuring impacts of IAS and collecting experimental data.
One area in which CS has clearly seen an increase in contributions is the domain of alien species (AS) science and policy Roy et al. 2018;Schade et al. 2019;Johnson et al. 2020). Alien species are defined as species introduced into a new geographic range by human intervention, either intentionally or by accident (Bellard et al. 2016). While AS may have a positive, neutral, or negative impact on their new environment (Goodenough 2010;Cox and Lima 2006), the term invasive alien species (IAS) refers to species whose introduction and spread has been found to threaten or adversely impact global biodiversity, ecosystem services, society and the economy (IPBES 2019; Seebens et al. 2017Seebens et al. , 2020EU Regulation 1143. Concerns over the impacts of IAS have led to policy responses internationally, nationally, and locally. For example, the UN's Sustainable Development Goals (SDGs) include a specific target on IAS (target 15.8). Similarly, in 2014, the European Union published the EU Regulation 1143/2014 to control the spread of IAS in all member states through prevention, early detection, rapid eradication, and management. This Regulation identifies a list of IAS of Union concern which pose a threat to biodiversity and require concerted action at the European Union level.
Accessible information on these IAS and implementation of associated policies is provided by the European Alien Species Information Network (EASIN; Katsanevakis et al. 2015;. The core function of this system is to gather and integrate data on AS occurring in Europe from data partners and scientific literature (Katsanevakis et al. 2012). Data originate from official monitoring programmes and research projects, but also derive from several IASfocussed CS projects active throughout Europe. These projects either deliver data to EASIN directly or publish to open data repositories like the Global Biodiversity Information Facility (GBIF), where they are harvested by the system.
The data gathered through IAS-focussed projects are very actionable, since they hold potential for use in early warning and rapid response, control programmes at various spatial scales, and policy implementation. Citizen science is especially valuable in an IAS context since tackling the spread of these species necessitates upscaled recording, improved understanding of the IAS problem and increased awareness at all levels of society, objectives for which CS is well suited. Ultimately, citizens who become involved in IAS CS projects gain a voice in promoting decision-making and policy implementation, thereby supporting the development of IAS policies (Groom et al. 2019). However, there is no updated and systematic analysis of IAS CS projects across Europe. This would allow a better understanding of the potential reach and gaps of such projects for European science and policy. Here, we present the first comprehensive overview of European IAS CS initiatives, comparing regional differences across Europe. Contrary to earlier work (Johnson et al. 2020), we focus on European AS-specific CS projects and journal publication is not used as a criterion for inclusion. Since Europe adopted a common Regulation on IAS (the above mentioned EU Regulation 1143/2014) we wanted to assess the policy relevance of projects with a particular emphasis on the implementation of this Regulation. We further evaluated the performance of projects considering their numbers of participants, number of AS records they yield and the publications derived from them, in order to understand how various parameters contribute to engagement.

Data Collection
This survey was developed within the scope of COST Action CA17122 -"Increasing understanding of alien species through citizen science (Alien-CSI)", which includes participants from all EU member states and a few neighboring countries. This COST Action sets out six research coordination objectives, to be first approached through a European wide analysis of existing IAS CS initiatives (Roy et al., 2018).
The first version of the survey was tested, revised and validated in a workshop attended by representatives from 25 countries in the COST Action. The survey (Price-Jones et al. 2021) was shared as a Google Form with all COST Action participants, and disseminated online.
Responses were collected from June 27, 2019 to April 6, 2020. For each country, existing CS projects involving AS were contacted and requested to complete the survey. All projects are/were active in EU member states and/or neighbouring countries. A list of projects was compiled, including from a web search and previously available lists of European CS projects (e.g., INNS, EASIN, Kus Veenvliet et al. 2019), and the missing projects in the survey database were contacted. Finally, to increase reach, the survey was also disseminated through the European Citizen Science Association (ECSA) newsletter and mailing list and respondents were asked to share it with colleagues and local networks via snowball sampling.
Survey questions and attribute values were developed using JRC metadata standards for CS projects (European Commission, Directorate-General for Environment 2018) and the project metadata model of PPSR Core, a set of global, transdisciplinary data and metadata standards for Public Participation in Scientific Research (PPSR Core). The survey included 62 questions (Price-Jones et al. 2021), in nine sections: 1) Contact information of the respondent; 2) General characterization of the project, including a brief summary, geographical scope, time scale, hosting entities, funding, etc.; 3) Information on project scope, including target audience, taxonomic and environmental scope, project aims, type of data collected, etc.; 4) Policy-related information, i.e., policy relevance and inclusion of species listed in the EU IAS Regulation; 5) Information on engagement, such as type of involvement of the general public in the design of the project, engagement methods and social media used, skills needed to participate and frequency of contributions; 6) Information on feedback and support provided to participants by the project, e.g., if the project provides materials for species identification, guidelines, training activities, information on how data from the project are used, feedback mechanisms and support; 7) Data quality and data management, namely validation mechanism for records, registration type, methods of recording, whether data are open and accessible to the general public, data form used to store data, data standards and data licence used, whether a public data management plan was in place; 8) Performance indicators of projects, namely usage of apps, number of participants and number of records, whether learning is assessed, number and type of publications using data from the project; and 9) Notes and remarks.

Preprocessing
Only projects that simultaneously fulfilled the following criteria were included in the analyses: 1) a clearly CS-focussed project; 2) AS included in the main scope; and 3) project developed in Europe (even if not exclusively). As such, national biodiversity networks and portals collecting data on all species were only considered if they had a clear AS focus. Projects needed to have specific forms of public engagement related to AS, so projects solely devoted to improving IAS policies but without a typical CS component (e.g., data collection using target groups, interaction with volunteers) were not considered. However, projects where data gathering was less relevant, but which had clear educational and outreach goals on IAS, were included.

Exploratory Analysis of Project Parameters
Of the nine survey sections, six asked for information about project parameters, or characteristics. These sections are: General characterization of the project, Information on project scope, Policy-related information, Information on engagement, Information on feedback and support, and Data quality and data management strategies. To explore the parameters of all surveyed projects, the frequency of each multiple choice or written answer was determined for each question within the above sections. Additionally, we were interested in determining if an association existed between target audience and target taxonomic group, or between target audience and target environment. Two-way chi square tests were conducted with a significance level of 0.05 to test for these associations.

Geographic Differences in Project Scope
In these series of analyses, we were interested in how the number of projects was affected by the scope of the project and whether the impact of project scope had a geographic component.
For this, we identified five parameters related to project scope: common targeted taxa, audience, environment and aim. Europe was divided into five regions: Northern Europe, Eastern Europe, Southern Europe, Western Europe, and the UK and Ireland. The UK is considered as a separate region with Ireland, due to its extensive history with CS (Silvertown 2009). These divisions are commonly used in ecology, with minor variability in the countries in each region (e.g. Bilton et al. 1998). To normalise according to the different number of inhabitants per region, the number of projects was expressed per million inhabitants using population data from the United Nations (United Nations 2019). Maps were created using ESRI ArcGIS Pro 2.7. For each of three project scope parameters, a two-way chi square test was conducted to test for association with geographical region. The tests were carried out with a significance level of 0.05.

Impact of engagement, feedback and support on project performance
To test whether parameters related to engagement, feedback and support had an effect on project performance, we selected 11 explanatory variables (four related to engagement, six related to feedback / support, and project duration) and defined three project performance indicators: the number of participants taking part in the project, the number of species observations (records) gathered by the project and the number of publications related to the project (Table 1). Three cumulative link models (CLMs) were conducted in RStudio version 3.3.3 (2017) using the package "ordinal" (Christensen 2018) to determine if engagement, feedback / support and project duration had a significant effect on performance indicators ( Table 1). Each of the three tests used a different performance indicator -number of participants, records and publications -as a response variable. All models were carried out with a significance level of 0.05. R code for these tests is published on Zenodo (Price-Jones et al. 2021).

Exploratory analysis of selected project parameters
Results are given as a percentage of answers that provided a definite response, as many questions did not have a 100% response rate, and respondents frequently answered "Unknown" or "Not applicable." The exact number of respondents of a specific response and the total number of respondents that provided a definite response are also given for each question.

General characterization of the project
In total 129 projects/initiatives completed data for the survey and of these 103 fitted the criteria for inclusion and were considered for analysis. Of the 26 that were excluded, seventeen were not AS-focussed, seven had no specific forms of public engagement on AS and two were duplicate entries. NGOs or private sources, or a combination of the above.

Project scope
Plants were the most common target taxonomic group (30%; 31/103; Figure 1a), the general public was the most common target audience (89%; 92/103; Figure 1b), and terrestrial habitats the most common environment analysed in the projects (57%; 59/103). There was no association between target audience and taxon (χ 2 = 54.52, df = 48, p = 0.24), but an association was observed between target audience and environment (χ 2 = 51.97, df = 30, p = 0.0077). Two trends in the data included the prevalence of terrestrial projects aimed at land managers, and freshwater and marine projects aimed at fishermen. The marine environment was also the environment type most frequently involving scientists and students.
84% of projects (87/103) focussed solely on AS and 9% (9/103) focussed partially on AS; 7% (7/103) responded that AS were not the main focus, yet AS data were collected and received some emphasis. Most projects had multiple aims, the most common being mapping of AS distribution ( Figure 1c). Most projects also collected more than one type of data, with species presence and/or abundance being the most common.

Policy-related information
75% of projects (59/79) claimed to have policy relevance, with 79% (77/97) including species on the List of IAS of Union concern (EU Regulation 1143/2014), whether exclusively or partially.

Information on engagement
In terms of project design, 39% of projects ( projects that used social media and stated the platform, Facebook was the most popular platform, used by 65% of projects (63/96), but Twitter, Instagram and YouTube were also used. Almost 95% of projects (94/99) responded that participants needed "None" or "Limited" skills nor prior knowledge to participate.

Information on feedback and support
The number of projects that provided species identification materials, guidelines, training, sighting maps, active informing, feedback and support is shown in

Project performance
The usage of applications developed or adopted by the project, number of participants, number of records and number of publications all show a distribution of responses that peaked in lower numbers and fell off quickly at higher numbers (

Geographic differences in project scope
According to responses to our survey, the UK had more AS CS projects (21) than any other country, followed by Italy (13), Portugal (9) and France (9) (Figure 2a). However, when project counts per region are normalized by population, Northern Europe has the highest ratio, followed by the UK and Ireland, Southern Europe, Eastern Europe and Western Europe ( Figure   2b). There was no association between geographic region and the target audience (χ 2 = 21.66, df = 24, p = 0.60), taxon (χ 2 = 33.44, df = 32, p = 0.40) or environment (χ 2 = 25.03, df = 20, p = 0.20).

Project Scope and Regional Variation
The dominance of national projects in our results is consistent with that observed for other research on management of biological invasions (Hulme et al. 2008). Several factors contribute to this tendency, including nationally-derived funding, differing degrees to which countries are exposed or aware of AS, species alien in one country being native to another, logistical convenience (Hulme et al. 2008) and uneven distribution of expertise (Hulme et al. 2009). However, international coordination is necessary to better protect native ecosystems from IAS (Perrings et al. 2010;Katsanevakis et al. 2013), as reflected by international agreements, from the Convention on Biological Diversity to EU Regulation 1143/2014.
Most projects target the general public, which is in line with informing (Genovesi et al. 2015) and engaging with and inspiring a passion for nature in as many participants as possible (Roy et al. 2015). Plants and insects are the most common target-taxa, possibly because both are broad and speciose groups that can be easily accessible, with many urban species. In addition, the prevalence of projects in the terrestrial environment reflects convenience for the public as reported for other CS projects (Aceves-Bueno et al. 2017), and is also highlighted by the association we found between target audience and target environment.
The most common aim is mapping of AS, and participants are often asked to submit species presence and/or abundance data; this type of data is useful for research and easy for citizen scientists to collect. Species presence in particular is easy to observe, report and validate (Hyder et al. 2015), and works well in conjunction with mapping (e.g. Malek et al. 2018;Kumar et al. 2019;Dissanayake et al. 2019).
The region with the most recorded projects is the UK and Ireland, reflecting a long history of CS in ecology (Silvertown 2009). After the UK, more projects in Western and Southern Europe may reflect a higher level of IAS awareness in these regions due to a relatively higher number of funded IAS projects (e.g., LIFE projects in Italy). On the other hand, a low number of projects, e.g., in the Netherlands, may be explained by a single dominant national biodiversity portal not being included in our survey. Additionally, there is a possible language bias, if projects from non-English speaking countries were not reached or not motivated to participate in the survey, which was only available in English.

Data Quality and Management
Studies evaluating data quality and management in CS projects sometimes have contradictory conclusions (Crall et al. 2011). Overall, volunteer contributions have been regarded favourably by scientists: e.g., 73% of papers positively described in analysis by Aceves-Bueno et al. (2017), but these authors concluded that differences between volunteer data and professional data were significant in 38.4% of projects, necessitating agreement on what magnitude of difference is acceptable. Nevertheless, other studies showed that models trained with data from the public vastly increased the predicted spatial distribution of IAS (de Sá et al. 2019). Our analysis shows that the majority of CS projects focusing on the recording of AS in Europe have a verification step undertaken by experts, with some other projects using either peer validation, automated procedures, or a combination of the three. As some projects have indicated their data are used for removal or management of highly regulated species, correct species identification is of utmost importance. However, little information is available on the exact nature and quality of the data verification, including on the interaction between observers and validators. The most prominent approaches for validation of CS data are peer and expert validation, often aided by automatic filtering techniques . Clearly, this is also the case for IAS CS in Europe .
Data generated by CS are often referred to as dark data: unreproducible, becoming more valuable over time, and at high risk of being lost (Costello and Wieczorek 2014). As such, implementation of a well-defined data management plan (DMP) can be used to prevent such loss of data. Nonetheless, few of the surveyed projects claimed to have a DMP. Many CS projects are relatively small scale and probably lack experience and/or access to tools for data management planning, which confirms earlier findings (Schade et al. 2017). Data management planning could improve the accessibility of data, an important component of FAIR (Findability, Accessibility, Interoperability, and Reusability) data management (Wilkinson et al. 2016;Reyserhove et al. 2020).
Specifically with regards to AS, openness of data is important to unlock their full potential for science, policy and management (Groom et al. , 2017a. However, although some projects deposit their data on national or institutional repositories, less than one third open up their data by making them freely available on an open data repository, e.g., through GBIF publication. We also found that most AS CS projects produced peer-reviewed papers, but these were not necessarily Open Access; however, most produced scientific communications aimed at the public. Reasons for avoiding open data may be multiple, e.g., projects that choose not to share data might do so due to licensing issues, funding limitations, technical barriers or out of concerns for the vulnerability of rare species or the privacy of the participants (Ganzevoort et al. 2017). Ganzevoort et al. (2017) found that half of the citizen scientists they surveyed believed that data collected by the CS organisation was a public good, but only 12.3% supported unconditional use. The question of data ownership is complex and can be addressed in legal terms by choice of license. We found that 92.3% of projects that provided license information had a license allowing public use. Overall, these parameters around data accessibility are consistent with findings of Wiggins and Crowston (2011) and Schade and Tsinaraki (2016), indicating a willingness to provide access to data.

Optimisation of Engagement
We anticipated that higher levels of feedback, support and engagement would improve performance, e.g., the number of participants, records and publications, through the generation of commitment and empowerment. However, results were somewhat inconsistent: as expected, Only 39% of the projects were designed collaboratively, thus in most cases citizens were contributing in a predetermined way (usually data collection: Wiggins and Crowston 2011).
Even so, a priori fewer projects were expected to be collaborative (e.g., Pocock et al. 2017 analysed more than 500 ecological and environmental CS projects and only 4% were collaborative) and so we suspect that this question may have been misunderstood. We define a collaboratively-designed project as a project with citizen scientist participation in the initial conception of the project and all subsequent steps. In contrast, the 39% of respondents who claimed collaborative project design may have considered lesser roles, such as feedback from participants on project design, as collaboration.
Surveyed projects mostly required low levels of time commitment for learning and participation, possibly recognizing that most participants in CS projects are amateur observers (Bonney et al. 2009). From the authors' own experience, even though many projects target the general public, in reality many of the participants do have some level of expertise in the taxonomic group they report. Wandersee and Schüssler (1998) note that the general public generally do not notice plants ("plant blindness"), suggesting that contributors to plant-focussed CS projects already have some knowledge about the taxon. Nonetheless, few projects assumed that citizens possessed a deeper understanding, such as the ability to distinguish between similar species (Rowley et al. 2019). Like the requirement of prior expertise, a demanding contribution schedule may impede participation, explaining the prevalence of projects requiring one-off or irregular contribution. The pattern in these parameters appears consistent with the result that few projects we assessed offer learning assessment.
Another unexpected result was that projects with limited or no skill requirements yielded a significantly lower number of records and publications. Possibly, participants with advanced skill levels, having already invested the time in learning, had a stronger commitment to contributing. This should not be taken as an argument towards raising thresholds, without serious consideration of whether it is warranted. Encouraging anyone to participate is highly relevant to the goal of reconnecting people with nature (Devictor et al. 2010) and increasing the chances of prevention and early-detection of IAS. Following engagement, projects must encourage continued participation (Penner 2002). To this end, the provision of different kinds of feedback and support presented a mix of positive and negative impacts. First, provision of a map positively impacted participation, records and publications, and provision of training positively impacted the number of publications. These both provide support for social exchange theory, which dictates that a demonstration of investment in participants by the project's organisers will encourage participants to reciprocate (Meek et al. 2011). Additionally, publicly displayed maps allow recognition of citizen scientists' efforts (Williams and DeSteno 2008;Crowston and Prestopnik 2013). The number of publications using data from the project might be an underestimation and could be highly biased by the level of knowledge of the respondents. To overcome this, future work could use the contingence of openly published datasets and link their identifiers to published scientific work. Data aggregators like GBIF consistently check for use of published and downloaded data in scientific publications and link those through data citation using digital object identifiers.
Finally, the majority of projects used an internet-based engagement method, such as a website or social media, reflecting the ubiquity of these technologies in Europe (Kemp 2021).
Social media was also shown to have a positive impact on the number of publications.
Organisations exploiting the internet and the most-used social media platform, Facebook (Kemp 2021), can clearly reach larger audiences than ones relying on print or radio. Alongside mobile applications, these strategies may help to recruit youth, as younger age groups are less wellrepresented in CS projects (Geoghegan et al. 2016). There is a recognized need to encourage children to go out and explore the natural world, not only to inspire a new generation of citizen scientists, but also so they can get to know and care for it (MacPhail and Colla 2020). However, if digital communication methods are not complemented by alternative strategies, participants who are not technologically able may become excluded (Encarnação et al. 2021).

Applications and Recommendations
Several lessons can be drawn from the results of our survey. First, a large number of CS projects apparently have not yet opened up their data. Open data publication maximizes the use of the data in policy processes, such as their use by EASIN in the implementation of the EU IAS Regulation ) and provides better return to CS participants on their contribution and value of their data. A clear recommendation from our work is therefore to actively support the flow of data from European citizen science projects to EASIN through open data publication. To this end, mechanisms should be installed to promote data sharing and interoperability. The adoption of data standards, including agreed invasion frameworks and terminology (Groom et al. 2019), is a part of this process. Major research networks or institutions could play an important role in facilitating this process, and the application of FAIR data principles could be made a prerequisite for funding CS projects. At a more practical level, active outreach to those projects, exploring their barriers to data sharing and providing technical support to implement open data flows can improve the use of IAS data in European policy.
One partial solution to openness and data management issues might be the drafting of DMPs. Strikingly, many projects are missing a DMP, but this is clearly needed to facilitate better storage, maintenance and use of data. As part of the DMP, strategies can be designed to make data openly accessible, for example on the platform GBIF. Relatively few respondents provided information about their scientific outputs, and further inspection revealed that there is often no information on project web pages about whether they publish their datasets on GBIF.
Furthermore, GBIF does often not reliably allow data to be tracked back to the project where it originated. Such practices may arise from privacy concerns, which may be circumvented by informing participants how and by whom their contributions are used prior to open data publication (Ganzevoort et al. 2017).
To further improve outreach and onboarding of new citizen scientists, and sustained participation, our results suggest that the provision of maps with sightings and the provision of training are important. Additionally, although our results were confusing concerning benefits from collaborative project design, other studies show that this type of design may be motivational (Geoghegan et al. 2016), and as such we recommend that, when possible, the public should be involved from the conception of the project and not only as data collectors. Future work could also be undertaken to compare the performance of different validation procedures and provide recommendations to new projects to improve data quality.
Our results show an increasing number of new AS CS projects in the last few years that contribute to IAS mapping and policy implementation. Clearly, some regions still hold untapped potential for new CS initiatives related to AS. In light of their common goal, we recommend that careful consideration should be made when considering new projects to avoid overlap and duplication of investments. Given that many CS projects are confronted with the issue of financial sustainability, we should instead promote better use and harmonization of projects and technologies already publicly available and customizable, such as iNaturalist, PlantNet or the "Invasive Alien Species Europe" app from EASIN. Building new initiatives onto an existing user base is also more efficient . Existing projects may also be improved and made accessible to new audiences through language translation or simplification, and through tailoring of aims and species lists to geographic regions (e.g., Invasive Alien Species in Europe application; Trichkova et al. 2021).
Finally, the similarity of goals across borders also signals that international cooperation is feasible, arguably becoming essential, given the global nature of biodiversity goals. The UN's SDGs provide an excellent model for how CS can be relevant to setting and achieving goals at a global level. Although SDGs were not initially developed with CS in mind, data gathered through CS can be used directly for feeding SDG indicators (Fritz et al. 2019), can increase the temporal and spatial scale of data collection ) and can engage people with science and the environment (Pocock et al. 2014). Nonetheless, Fraisl et al. (2020) noted poor alignment of CS initiatives with target 15.8 on IAS.

Conclusions
The many CS projects contributing to IAS mapping and policy implementation described in our paper clearly demonstrate that CS for IAS is currently booming and that the information on the presence, abundance and spread of IAS gathered through CS is highly relevant for (inter)national legislation. The EU IAS Regulation can likewise benefit from international cooperation in IAS research and in fact a majority of IAS CS projects have a focus on IAS of Union Concern. Nonetheless, our results show that less than half of projects receive government funding. Considering the massive contribution and untapped potential of the "billion eyes on the ground" to the implementation of IAS policies, and also IAS research and management, strategies should be developed to support regions where AS CS is only emerging, to strengthen the links between CS projects and networks around the EU Regulation and to provide networking opportunities where CS projects can exchange experiences to further foster an active AS CS landscape across the continent.