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

A local community on a global collective intelligence platform: A case study of individual preferences and collective bias in ecological citizen science

The collective intelligence of crowds could potentially be harnessed to address global challenges, such as biodiversity loss and species' extinction. For wisdom to emerge from the crowd, certain conditions are required. Importantly, the crowd should be diverse and people's contributions should be independent of one another. Here we investigate a global citizen-science platform-iNaturalist-on which citizens report on wildlife observations, collectively producing maps of species' spatiotemporal distribution. The organization of global platforms such as iNaturalist around local projects compromises the assumption of diversity and independence, and thus raises concerns regarding the quality of such collectively-generated data. We spent four years closely immersing ourselves in a local community of citizen scientists who reported their wildlife sightings on iNaturalist. Our ethnographic study involved the use of questionnaires, interviews, and analysis of archival materials. Our analysis revealed observers' nuanced considerations as they chose where, when, and what type of species to monitor, and which observations to report. Following a thematic analysis of the data, we organized observers' preferences and constraints into four main categories: recordability, community value, personal preferences, and convenience. We show that while some individual partialities can "cancel each other out", others are commonly shared among members of the community, potentially biasing the aggregate database of observations. Our discussion draws attention to the way in which widely-shared individual preferences might manifest as spatial, temporal, and crucially, taxonomic biases in the collectively-created database. We offer avenues for continued research that will help better understand-and tackle-individual preferences, with the goal of attenuating collective bias in data, and facilitating the generation of reliable state-of-nature reports. Finally, we offer insights into the broader literature on biases in collective intelligence systems.

Urbanization reduces diversity, simplifies community and filter bird species based on their functional traits in a tropical city

Understanding how organisms are coping with major changes imposed by urban intensification is a complex task. In fact, our understanding of the impacts of urbanization on biodiversity is scarce in the global south compared to the north. In this study, we evaluated how bird communities are affected by impact of urban intensification in a tropical city. Thus, we assessed whether increased urban intensification 1) jeopardizes bird diversity (taking into account taxonomic-TD, phylogenetic-PD, and functional-FD dimensions), 2) drives changes in bird community composition and enables the detection of indicator species of such impact, and 3) leads to changes in bird functional traits linked to reproduction, resource acquisition, and survival. We found that urban intensification has a direct impact on the bird community, reducing all three types of diversity. Communities in areas of greater urban intensity are represented by fewer species, and these species are PD and FD less distinct. In addition, we detected at least ten species of areas of lower urban intensity that proved to be more sensitive to urban intensification. With regard to bird traits, we found no significant responses from reproductive, habitat use and feeding variables. Body weight and tail length were the only variables with significant results, with higher urbanization intensity areas selecting for species with lower weights and longer tails. Given the global biodiversity loss we are observing, this information can guide urban managers and planners in designing urban landscapes to maintain biodiversity in cities.

Color aberration in malachite kingfishers: Insights from community science observations in Queen Elizabeth National Park, Uganda

Color aberrations in birds corresponds with important ecological functions, including thermoregulation and physiological impacts, camouflage and increased predation, and social interactions with conspecifics. Color aberrations in birds have been reported frequently in the scientific literature, but aberrations in many species remain undocumented or understudied. We investigated records of leucism in malachite kingfishers (Corythornis cristatus) from observations of community scientists on iNaturalist and eBird in Uganda. Leucistic kingfishers were only observed within the Queen Elizabeth National Park (QENP), Uganda. When considering all observations of malachite kingfishers that included photographs within the QENP, leucistic individuals accounted for 13.0% and 10.4% of total malachite kingfisher observations within the study area from iNaturalist and eBird, respectively. Leucistic observations were recorded from September 2015 through February 2017, making up 60.0% and 68.2% of observations of malachite kingfishers within the study area from iNaturalist and eBird during that time, respectively. The localized and short documentation period suggests observations represent a single individual, while the high observation rate likely corresponds with collection bias due to the novelty of the individual. Our findings help to better understand the ecological importance and potential consequences for color-aberrant individuals, although color aberration did not appear to inhibit our subject's ability to find a mate. Our work also highlights how participatory science can promote the documentation of color-aberrant individuals in wild populations, although it poses challenges when trying to estimate abundance.

Extracting secondary data from citizen science images reveals host flower preferences of the Mexican grass-carrying wasp Isodontia mexicana in its native and introduced ranges

We investigated the plant-pollinator interactions of the Mexican grass-carrying wasp Isodontia mexicana-native to North America and introduced in Europe in the 1960s-through the use of secondary data from citizen science observations. We applied a novel data exchange workflow from two global citizen science platforms, iNaturalist and Pl@ntNet. Images from iNaturalist of the wasp were used to query the Pl@ntNet application to identify possible plant species present in the pictures. Simultaneously, botanists manually identified the plants at family, genus and species levels and additionally documented flower color and biotic interactions. The goals were to calibrate Pl@ntNet's accuracy in relation to this workflow, update the list of plant species that I. mexicana visits as well as its flower color preferences in its native and introduced ranges. In addition, we investigated the types and corresponding frequencies of other biotic interactions incidentally captured on the citizen scientists' images. Although the list of known host plants could be expanded, identifying the flora from images that predominantly show an insect proved difficult for both experts and the Pl@ntNet app. The workflow performs with a 75% probability of correct identification of the plant at the species level from a score of 0.8, and with over 90% chance of correct family and genus identification from a score of 0.5. Although the number of images above these scores may be limited due to the flower parts present on the pictures, our approach can help to get an overview into species interactions and generate more specific research questions. It could be used as a triaging method to select images for further investigation. Additionally, the manual analysis of the images has shown that the information they contain offers great potential for learning more about the ecology of an introduced species in its new range.

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.

Not all who wander are lost: Trail bias in community science

The exponential growth and interest in community science programs is producing staggering amounts of biodiversity data across broad temporal and spatial scales. Large community science datasets such as iNaturalist and eBird are allowing ecologists and conservation biologists to answer novel questions that were not possible before. However, the opportunistic nature of many of these enormous datasets leads to biases. Spatial bias is a common problem, where observations are biased towards points of access like roads and trails. iNaturalist-a popular biodiversity community science platform-exhibits strong spatial biases, but it is unclear how these biases affect the quality of biodiversity data collected. Thus, we tested whether fine-scale spatial bias due to sampling from trails affects taxonomic richness estimates. We compared timed transects with experienced iNaturalist observers on and off trails in British Columbia, Canada. Using generalized linear mixed models, we found higher overall taxonomic richness on trails than off trails. In addition, we found more exotic as well as native taxa on trails than off trails. There was no difference between on and off trail observations for species that are rarely observed. Thus, fine-scale spatial bias from trails does not reduce the quality of biodiversity measurements, a promising result for those interested in using iNaturalist data for research and conservation management.

Photographs as an essential biodiversity resource: drivers of gaps in the vascular plant photographic record

The photographic record is increasingly becoming an important biodiversity resource for primary research and conservation monitoring. However, globally, there are important gaps in this record even in relatively well-researched floras. To quantify the gaps in the Australian native vascular plant photographic record, we systematically surveyed 33 sources of well-curated species photographs, assembling a list of species with accessible and verifiable photographs, as well as the species for which this search failed. Of 21 077 Australian native species, 3715 lack a verifiable photograph across our 33 surveyed resources. There are three major geographic hotspots of unphotographed species in Australia, all far from current population centres. Many unphotographed species are small in stature or uncharismatic, and many are also recently described. The large number of recently described species without accessible photographs was surprising. There are longstanding efforts in Australia to organise the plant photographic record, but in the absence of a global consensus to treat photographs as an essential biodiversity resource, this has not become common practice. Many recently described species are small-range endemics and some have special conservation status. Completing the botanical photographic record across the globe will facilitate a virtuous feedback loop of more efficient identification, monitoring and conservation.

Integrating Citizen Scientist Data into the Surveillance System for Avian Influenza Virus, Taiwan

The continuing circulation and reassortment with low-pathogenicity avian influenza Gs/Gd (goose/Guangdong/1996)-like avian influenza viruses (AIVs) has caused huge economic losses and raised public health concerns over the zoonotic potential. Virologic surveillance of wild birds has been suggested as part of a global AIV surveillance system. However, underreporting and biased selection of sampling sites has rendered gaining information about the transmission and evolution of highly pathogenic AIV problematic. We explored the use of the Citizen Scientist eBird database to elucidate the dynamic distribution of wild birds in Taiwan and their potential for AIV exchange with domestic poultry. Through the 2-stage analytical framework, we associated nonignorable risk with 10 species of wild birds with >100 significant positive results. We generated a risk map, which served as the guide for highly pathogenic AIV surveillance. Our methodologic blueprint has the potential to be incorporated into the global AIV surveillance system of wild birds.

Citizen science plant observations encode global trait patterns

Global maps of plant functional traits are essential for studying the dynamics of the terrestrial biosphere, yet the spatial distribution of trait measurements remains sparse. With the increasing popularity of species identification apps, citizen scientists contribute to growing vegetation data collections. The question emerges whether such opportunistic citizen science data can help map plant functional traits globally. Here we show that we can map global trait patterns by complementing vascular plant observations from the global citizen science project iNaturalist with measurements from the plant trait database TRY. We evaluate these maps using sPlotOpen, a global collection of vegetation plot data. Our results show high correlations between the iNaturalist- and sPlotOpen-based maps of up to 0.69 (r) and higher correlations than to previously published trait maps. As citizen science data collections continue to grow, we can expect them to play a significant role in further improving maps of plant functional traits.

Clavis: An open and versatile identification key format

The skills and knowledge needed to recognize and classify taxa are becoming increasingly scarce in the scientific community. At the same time, it is clear that these skills are strongly needed in biodiversity monitoring for management and conservation, especially when carried out by citizen scientists. Formalizing the required knowledge in the form of digital identification keys is one way of making such knowledge more available for professional and amateur observers of biodiversity. In this paper we describe Clavis, an open and versatile data format for capturing the knowledge required for taxon identification through digital keys, allowing for a level of detail beyond that of any current key format. We present the format independently from any particular implementation, as our aim is for Clavis to serve as a basis for interoperable tools and interfaces serving different needs and actors.

Evaluating the impact of turbidity, precipitation, and land use on nutrient levels and atrazine concentrations in Illinois surface water as determined by citizen scientists

The objective of this study was to evaluate the impact of turbidity, precipitation, land use, and five-week variation on nutrient levels and atrazine concentrations across Illinois state. To acquire the greatest number of samples in a cost and time-sensitive manner, data were collected by citizen scientists. Volunteers collected data regarding five water quality metrics: nitrites, nitrates, phosphates, atrazine, and turbidity once per week from April 19 until May 17, 2017. A subset (24 %) of volunteers also collected turbidity measurements. Data regarding precipitation was obtained from the Community Collaborative Rain, Hail and Snow Network (CoCoRaHS), a long-standing grassroots volunteer network of backyard weather observers. Three ordinal regression analyses were performed: one without a blocking effect, a second with week as a blocking effect, and a third with watershed as a blocking effect. In all cases, turbidity was significantly associated with elevated levels of nitrate (Pseudo R²-0.48 to 0.94) and phosphate (Pseudo R²-0.60 to 0.80), while precipitation was significantly associated with elevated levels of nitrate (Pseudo R²-0.25 to 0.35). While analyzing five-week variation, the nitrite and nitrate levels, but not phosphate or atrazine, tended to increase at each site. Further, nitrite and nitrate levels significantly varied between the four land uses - agricultural, urban, suburban, and park. When data were analyzed by the three most well-sampled watersheds, Kankakee, Des Plaines, and Chicago, it was identified that the nutrient levels in the Kankakee and Chicago watersheds were significantly elevated relative to the Des Plaines watershed. Finally, cluster analysis identified that clusters dominated by agricultural land, and to a lesser extent suburban land use, had the most elevated nutrient concentration and the greatest frequency of atrazine hits. Data collected by citizen scientists can provide insight into the geospatial variability of nutrients and agrichemicals and can do so across large geographies.

Citizen Science and Monitoring Forest Pests: a Beneficial Alliance?

Purpose of the Review

One of the major threats to tree health, and hence the resilience of forests and their provision of ecosystem services, is new and emerging pests. Therefore, forest health monitoring is of major importance to detect invasive, emerging and native pest outbreaks. This is usually done by foresters and forest health experts, but can also be complemented by citizen scientists. Here, we review the use of citizen science for detection and monitoring, as well as for hypothesis-driven research and evaluation of control measures as part of forest pest surveillance and research. We then examine its limitations and opportunities and make recommendations on the use of citizen science for forest pest monitoring.

Recent Findings

The main opportunities of citizen scientists for forest health are early warning, early detection of new pests, monitoring of impact of outbreaks and scientific research. Each domain has its own limitations, opportunities and recommendations to follow, as well as their own public engagement strategies. The development of new technologies provides many opportunities to involve citizen scientists in forest pest monitoring. To enhance the benefits of citizen scientists' inclusion in monitoring, it is important that they are involved in the cocreation of activities.

Summary

Future monitoring and research may benefit from tailor-made citizen science projects to facilitate successful monitoring by citizen scientists and expand their practice to countries where the forest health sector is less developed. In this sense, citizen scientists can help understand and detect outbreaks of new pests and avoid problems in the future.

Identifying, reducing, and communicating uncertainty in community science: a focus on alien species

Community science (also often referred to as citizen science) provides a unique opportunity to address questions beyond the scope of other research methods whilst simultaneously engaging communities in the scientific process. This leads to broad educational benefits, empowers people, and can increase public awareness of societally relevant issues such as the biodiversity crisis. As such, community science has become a favourable framework for researching alien species where data on the presence, absence, abundance, phenology, and impact of species is important in informing management decisions. However, uncertainties arising at different stages can limit the interpretation of data and lead to projects failing to achieve their intended outcomes. Focusing on alien species centered community science projects, we identified key research questions and the relevant uncertainties that arise during the process of developing the study design, for example, when collecting the data and during the statistical analyses. Additionally, we assessed uncertainties from a linguistic perspective, and how the communication stages among project coordinators, participants and other stakeholders can alter the way in which information may be interpreted. We discuss existing methods for reducing uncertainty and suggest further solutions to improve data reliability. Further, we make suggestions to reduce the uncertainties that emerge at each project step and provide guidance and recommendations that can be readily applied in practice. Reducing uncertainties is essential and necessary to strengthen the scientific and community outcomes of community science, which is of particular importance to ensure the success of projects aimed at detecting novel alien species and monitoring their dynamics across space and time.

Enhancing Our Understanding of Ladybirds in Ireland – A Case Study of the Use of Citizen Science

Citizen science is increasingly recognised as a useful approach to contribute to biodiversity awareness while generating data and creating meaningful interactions among citizens and professional scientists. In a number of countries, academic institutions have led the way in developing ladybird projects, incorporating citizen science, to increase distributional data on the species, as well as a greater understanding of ladybird ecology. The current research evaluates the role of citizen science campaigns in collecting biological distributional data in Ireland, using ladybirds as a case example, examining if citizen science records and the use of photographs can provide a valuable insight into our understanding of ladybird ecology. From April 2020 to June 2021, a citizen science programme engaged with the wider public, via social media networks to increase the number of ladybird records and assist in establishing national data baselines, since ladybird rcording is a relatively new phenomenon in Ireland. This case study finds that citizen science projects using social media can increase the number of records of ladybirds and provide useful information on species distribution patterns. There were a disproportionate number of records from areas of high human population, resulting in a bias towards urban centres. Photographs submitted by members of the public provided a limited amount of information on food plants but indicated that ladybirds are valued as a species of interest to the public. These images can also help to increase records of the less common species, especially as members of the public may not be able to easily distinguish between different species of ladybirds.

Where to search: the use of opportunistic data for the detection of an invasive forest pest

Early detection is important for the management of invasive alien species. In the last decade citizen science has become an important source of such data. Here, we used opportunistic records from the “LIFE ARTEMIS” citizen science project, in which people submitted records from places where they observed tree pests, to understand the distribution of a rapidly-spreading forest pest: the oak lace bug (Corythucha arcuata) in Slovenia. These citizen science records were not distributed randomly. We constructed a species distribution model for C. arcuata that accounted for the biased distribution of citizen science by using the records of other tree pests and diseases from the same project as pseudo-absences (so-called constrained pseudo-absences), and compared this to a model with pseudo-absences selected randomly from across Slovenia. We found that the constrained pseudo-absence model showed that C. arcuata was more likely to be found in east, in places with more oak trees and at lower elevations, and also closer to highways and railways, indicating introduction and dispersal by accidental human transport. The outputs from the model with random pseudo-absences were broadly similar, although estimates from this model tended to be higher and less precise, and some factors that were significant (proximity to minor roads and human settlements) were artefacts of recorder bias, showing the importance of taking the distribution of recording into account wherever possible. The finding that C. arcuata is more likely to be found near highways allows us to design advice for where future citizen science should be directed for efficient early detection.

Decision-making of citizen scientists when recording species observations

Citizen scientists play an increasingly important role in biodiversity monitoring. Most of the data, however, are unstructured—collected by diverse methods that are not documented with the data. Insufficient understanding of the data collection processes presents a major barrier to the use of citizen science data in biodiversity research. We developed a questionnaire to ask citizen scientists about their decision-making before, during and after collecting and reporting species observations, using Germany as a case study. We quantified the greatest sources of variability among respondents and assessed whether motivations and experience related to any aspect of data collection. Our questionnaire was answered by almost 900 people, with varying taxonomic foci and expertise. Respondents were most often motivated by improving species knowledge and supporting conservation, but there were no linkages between motivations and data collection methods. By contrast, variables related to experience and knowledge, such as membership of a natural history society, were linked with a greater propensity to conduct planned searches, during which typically all species were reported. Our findings have implications for how citizen science data are analysed in statistical models; highlight the importance of natural history societies and provide pointers to where citizen science projects might be further developed.

Maximizing citizen scientists' contribution to automated species recognition

Technological advances and data availability have enabled artificial intelligence-driven tools that can increasingly successfully assist in identifying species from images. Especially within citizen science, an emerging source of information filling the knowledge gaps needed to solve the biodiversity crisis, such tools can allow participants to recognize and report more poorly known species. This can be an important tool in addressing the substantial taxonomic bias in biodiversity data, where broadly recognized, charismatic species are highly over-represented. Meanwhile, the recognition models are trained using the same biased data, so it is important to consider what additional images are needed to improve recognition models. In this study, we investigated how the amount of training data influenced the performance of species recognition models for various taxa. We utilized a large citizen science dataset collected in Norway, where images are added independently from identification. We demonstrate that while adding images of currently under-represented taxa will generally improve recognition models more, there are important deviations from this general pattern. Thus, a more focused prioritization of data collection beyond the basic paradigm that “more is better” is likely to significantly improve species recognition models and advance the representativeness of biodiversity data.

Temporal trends in opportunistic citizen science reports across multiple taxa

Opportunistic reporting of species observations to online platforms provide one of the most extensive sources of information about the distribution and status of organisms in the wild. The lack of a clear sampling design, and changes in reporting over time, leads to challenges when analysing these data for temporal change in organisms. To better understand temporal changes in reporting, we use records submitted to an online platform in Sweden (Artportalen), currently containing 80 million records. Focussing on five taxonomic groups, fungi, plants, beetles, butterflies and birds, we decompose change in reporting into long-term and seasonal trends, and effects of weekdays, holidays and weather variables. The large surge in number of records since the launch of the, initially taxa-specific, portals is accompanied by non-trivial long-term and seasonal changes that differ between the taxonomic groups and are likely due to changes in, and differences between, the user communities and observer behaviour.

Large-bodied birds are over-represented in unstructured citizen science data

Citizen science platforms are quickly accumulating hundreds of millions of biodiversity observations around the world annually. Quantifying and correcting for the biases in citizen science datasets remains an important first step before these data are used to address ecological questions and monitor biodiversity. One source of potential bias among datasets is the difference between those citizen science programs that have unstructured protocols and those that have semi-structured or structured protocols for submitting observations. To quantify biases in an unstructured citizen science platform, we contrasted bird observations from the unstructured iNaturalist platform with that from a semi-structured citizen science platform-eBird-for the continental United States. We tested whether four traits of species (body size, commonness, flock size, and color) predicted if a species was under- or over-represented in the unstructured dataset compared with the semi-structured dataset. We found strong evidence that large-bodied birds were over-represented in the unstructured citizen science dataset; moderate evidence that common species were over-represented in the unstructured dataset; strong evidence that species in large groups were over-represented; and no evidence that colorful species were over-represented in unstructured citizen science data. Our results suggest that biases exist in unstructured citizen science data when compared with semi-structured data, likely as a result of the detectability of a species and the inherent recording process. Importantly, in programs like iNaturalist the detectability process is two-fold-first, an individual organism needs to be detected, and second, it needs to be photographed, which is likely easier for many large-bodied species. Our results indicate that caution is warranted when using unstructured citizen science data in ecological modelling, and highlight body size as a fundamental trait that can be used as a covariate for modelling opportunistic species occurrence records, representing the detectability or identifiability in unstructured citizen science datasets. Future research in this space should continue to focus on quantifying and documenting biases in citizen science data, and expand our research by including structured citizen science data to understand how biases differ among unstructured, semi-structured, and structured citizen science platforms.

Observing the Observers: How Participants Contribute Data to iNaturalist and Implications for Biodiversity Science

The availability of citizen science data has resulted in growing applications in biodiversity science. One widely used platform, iNaturalist, provides millions of digitally vouchered observations submitted by a global user base. These observation records include a date and a location but otherwise do not contain any information about the sampling process. As a result, sampling biases must be inferred from the data themselves. In the present article, we examine spatial and temporal biases in iNaturalist observations from the platform's launch in 2008 through the end of 2019. We also characterize user behavior on the platform in terms of individual activity level and taxonomic specialization. We found that, at the level of taxonomic class, the users typically specialized on a particular group, especially plants or insects, and rarely made observations of the same species twice. Biodiversity scientists should consider whether user behavior results in systematic biases in their analyses before using iNaturalist data.

A Framework of Observer-Based Biases in Citizen Science Biodiversity Monitoring: Semi-Structuring Unstructured Biodiversity Monitoring Protocols

Citizen science, whereby ordinary citizens participate in scientific endeavors, is widely used for biodiversity monitoring, most commonly by relying on unstructured monitoring approaches. Notwithstanding the potential of unstructured citizen science to engage the public and collect large amounts of biodiversity data, observers’ considerations regarding what, where and when to monitor result in biases in the aggregate database, thus impeding the ability to draw conclusions about trends in species’ spatio-temporal distribution. Hence, the goal of this study is to enhance our understanding of observer-based biases in citizen science for biodiversity monitoring. Toward this goals we: (a) develop a conceptual framework of observers’ decision-making process along the steps of monitor – > record and share, identifying the considerations that take place at each step, specifically highlighting the factors that influence the decisions of whether to record an observation (b) propose an approach for operationalizing the framework using a targeted and focused questionnaire, which gauges observers’ preferences and behavior throughout the decision-making steps, and (c) illustrate the questionnaire’s ability to capture the factors driving observer-based biases by employing data from a local project on the iNaturalist platform. Our discussion highlights the paper’s theoretical contributions and proposes ways in which our approach for semi-structuring unstructured citizen science data could be used to mitigate observer-based biases, potentially making the collected biodiversity data usable for scientific and regulatory purposes.

Global abundance estimates for 9,700 bird species

Quantifying the abundance of species is essential to ecology, evolution, and conservation. The distribution of species abundances is fundamental to numerous longstanding questions in ecology, yet the empirical pattern at the global scale remains unresolved, with a few species' abundance well known but most poorly characterized. In large part because of heterogeneous data, few methods exist that can scale up to all species across the globe. Here, we integrate data from a suite of well-studied species with a global dataset of bird occurrences throughout the world-for 9,700 species (∼92% of all extant species)-and use missing data theory to estimate species-specific abundances with associated uncertainty. We find strong evidence that the distribution of species abundances is log left skewed: there are many rare species and comparatively few common species. By aggregating the species-level estimates, we find that there are ∼50 billion individual birds in the world at present. The global-scale abundance estimates that we provide will allow for a line of inquiry into the structure of abundance across biogeographic realms and feeding guilds as well as the consequences of life history (e.g., body size, range size) on population dynamics. Importantly, our method is repeatable and scalable: as data quantity and quality increase, our accuracy in tracking temporal changes in global biodiversity will increase. Moreover, we provide the methodological blueprint for quantifying species-specific abundance, along with uncertainty, for any organism in the world.

A real-world implementation of a nationwide, long-term monitoring program to assess the impact of agrochemicals and agricultural practices on biodiversity

Biodiversity has undergone a major decline throughout recent decades, particularly in farmland. Agricultural practices are recognized to be an important pressure on farmland biodiversity, and pesticides are suspected to be one of the main causes of this decline in biodiversity. As part of the national plan for reduction of pesticides use (Ecophyto), the French ministry of agriculture launched the 500 ENI (nonintended effects) monitoring program in 2012 in order to assess the unintended effects of agricultural practices, including pesticide use, on biodiversity represented by several taxonomic groups of interest for farmers. This long-term program monitors the biodiversity of nontargeted species (earthworms, plants, coleoptera, and birds), together with a wide range of annual data on agricultural practices (crop rotation, soil tillage, weed control, fertilizers, chemical treatments, etc.). Other parameters (e.g., landscape and climatic characteristics) are also integrated as covariates during the analyses. This monitoring program is expected to improve our understanding of the relative contribution of the different drivers of population and community trends. Here, we present the experience of setting up the 500 ENI network for this ambitious and highly complex monitoring program, as well as the type of data it collects. The issue of data quality control and some first results are discussed. With the aim of being useful to readers who would like to set up similar monitoring schemes, we also address some questions that have arisen following the first five years of the implementation phase of the program.

Effects of site-selection bias on estimates of biodiversity change

Estimates of biodiversity change are essential for the management and conservation of ecosystems. Accurate estimates rely on selecting representative sites, but monitoring often focuses on sites of special interest. How such site-selection biases influence estimates of biodiversity change is largely unknown. Site-selection bias potentially occurs across four major sources of biodiversity data, decreasing in likelihood from citizen science, museums, national park monitoring, and academic research. We defined site-selection bias as a preference for sites that are either densely populated (i.e., abundance bias) or species rich (i.e., richness bias). We simulated biodiversity change in a virtual landscape and tracked the observed biodiversity at a sampled site. The site was selected either randomly or with a site-selection bias. We used a simple spatially resolved, individual-based model to predict the movement or dispersal of individuals in and out of the chosen sampling site. Site-selection bias exaggerated estimates of biodiversity loss in sites selected with a bias by on average 300-400% compared with randomly selected sites. Based on our simulations, site-selection bias resulted in positive trends being estimated as negative trends: richness increase was estimated as 0.1 in randomly selected sites, whereas sites selected with a bias showed a richness change of -0.1 to -0.2 on average. Thus, site-selection bias may falsely indicate decreases in biodiversity. We varied sampling design and characteristics of the species and found that site-selection biases were strongest in short time series, for small grains, organisms with low dispersal ability, large regional species pools, and strong spatial aggregation. Based on these findings, to minimize site-selection bias, we recommend use of systematic site-selection schemes; maximizing sampling area; calculating biodiversity measures cumulatively across plots; and use of biodiversity measures that are less sensitive to rare species, such as the effective number of species. Awareness of the potential impact of site-selection bias is needed for biodiversity monitoring, the design of new studies on biodiversity change, and the interpretation of existing data.

Trends and gaps in the use of citizen science derived data as input for species distribution models: A quantitative review

Citizen science (CS) currently refers to the participation of non-scientist volunteers in any discipline of conventional scientific research. Over the last two decades, nature-based CS has flourished due to innovative technology, novel devices, and widespread digital platforms used to collect and classify species occurrence data. For scientists, CS offers a low-cost approach of collecting species occurrence information at large spatial scales that otherwise would be prohibitively expensive. We examined the trends and gaps linked to the use of CS as a source of data for species distribution models (SDMs), in order to propose guidelines and highlight solutions. We conducted a quantitative literature review of 207 peer-reviewed articles to measure how the representation of different taxa, regions, and data types have changed in SDM publications since the 2010s. Our review shows that the number of papers using CS for SDMs has increased at approximately double the rate of the overall number of SDM papers. However, disparities in taxonomic and geographic coverage remain in studies using CS. Western Europe and North America were the regions with the most coverage (73%). Papers on birds (49%) and mammals (19.3%) outnumbered other taxa. Among invertebrates, flying insects including Lepidoptera, Odonata and Hymenoptera received the most attention. Discrepancies between research interest and availability of data were as especially important for amphibians, reptiles and fishes. Compared to studies on animal taxa, papers on plants using CS data remain rare. Although the aims and scope of papers are diverse, species conservation remained the central theme of SDM using CS data. We present examples of the use of CS and highlight recommendations to motivate further research, such as combining multiple data sources and promoting local and traditional knowledge. We hope our findings will strengthen citizen-researchers partnerships to better inform SDMs, especially for less-studied taxa and regions. Researchers stand to benefit from the large quantity of data available from CS sources to improve global predictions of species distributions.

COVID-19 pandemic drives changes in participation in citizen science project "City Nature Challenge" in Tokyo

The COVID-19 pandemic has changed the way large citizen science events can be carried out-reducing gatherings of large groups and shifting toward individual, small-group, and online participation. This paper aims to describe changes in participant engagement in the City Nature Challenge (CNC) in Tokyo. The CNC is a four-day international event held in April to document biodiversity in cities using an online citizen science platform, iNaturalist. To assess the impact of COVID-19, we compared the number of participants, observations, species, and identification rates in 2019 (pre-pandemic) and 2020 (during the pandemic). We also used cluster analysis to elucidate participation patterns, and we assessed changes in the geographical distribution of observation sites. The results showed: (1) the number of participants and observations decreased by 63% and 68%, respectively; however, the number of species was almost the same in the two years, and the identification rate increased 154% in 2020 relative to 2019. (2) The most enthusiastic participants contributed in similar amounts in 2019 and 2020, but participation by less enthusiastic volunteers drastically declined. (3) The spatial distribution of observation sites changed from cluster-like to scattered. Understanding participant engagement during the pandemic could help to improve data quality, reduce geographical bias in observations, maintain records, and recruit more users in future years. Online citizen science could provide opportunities for many citizens to get outside and participate in conservation science during and after the pandemic.

Benchmark Bird Surveys Help Quantify Counting Accuracy in a Citizen-Science Database

The growth of biodiversity data sets generated by citizen scientists continues to accelerate. The availability of such data has greatly expanded the scale of questions researchers can address. Yet, error, bias, and noise continue to be serious concerns for analysts, particularly when data being contributed to these giant online data sets are difficult to verify. Counts of birds contributed to eBird, the world’s largest biodiversity online database, present a potentially useful resource for tracking trends over time and space in species’ abundances. We quantified counting accuracy in a sample of 1,406 eBird checklists by comparing numbers contributed by birders (N = 246) who visited a popular birding location in Oregon, USA, with numbers generated by a professional ornithologist engaged in a long-term study creating benchmark (reference) measurements of daily bird counts. We focused on waterbirds, which are easily visible at this site. We evaluated potential predictors of count differences, including characteristics of contributed checklists, of each species, and of time of day and year. Count differences were biased toward undercounts, with more than 75% of counts being below the daily benchmark value. Median count discrepancies were −29.1% (range: 0 to −42.8%; N = 20 species). Model sets revealed an important influence of each species’ reference count, which varied seasonally as waterbird numbers fluctuated, and of percent of species known to be present each day that were included on each checklist. That is, checklists indicating a more thorough survey of the species richness at the site also had, on average, smaller count differences. However, even on checklists with the most thorough species lists, counts were biased low and exceptionally variable in their accuracy. To improve utility of such bird count data, we suggest three strategies to pursue in the future. (1) Assess additional options for analytically determining how to select checklists that include less biased count data, as well as exploring options for correcting bias during the analysis stage. (2) Add options for users to provide additional information that helps analysts choose checklists, such as an option for users to tag checklists where they focused on obtaining accurate counts. (3) Explore opportunities to effectively calibrate citizen-science bird count data by establishing a formalized network of marquis sites where dedicated observers regularly contribute carefully collected benchmark data.

Drivers of spatio-temporal variation in mosquito submissions to the citizen science project 'Mückenatlas'

Intensified travel activities of humans and the ever growing global trade create opportunities of arthropod-borne disease agents and their vectors, such as mosquitoes, to establish in new regions. To update the knowledge of mosquito occurrence and distribution, a national mosquito monitoring programme was initiated in Germany in 2011, which has been complemented by a citizen science project, the ‘Mückenatlas’ since 2012. We analysed the ‘Mückenatlas’ dataset to (1) investigate causes of variation in submission numbers from the start of the project until 2017 and to (2) reveal biases induced by opportunistic data collection. Our results show that the temporal variation of submissions over the years is driven by fluctuating topicality of mosquito-borne diseases in the media and large-scale climate conditions. Hurdle models suggest a positive association of submission numbers with human population, catch location in the former political East Germany and the presence of water bodies, whereas precipitation and wind speed are negative predictors. We conclude that most anthropogenic and environmental effects on submission patterns are associated with the participants’ (recording) behaviour. Understanding how the citizen scientists’ behaviour shape opportunistic datasets help to take full advantage of the available information.

A Framework for Classifying Participant Motivation that Considers the Typology of Citizen Science Projects

Citizen science, the participation of the public in scientific projects, is growing significantly, especially with technological developments in recent years. Volunteers are the heart of citizen science projects; therefore, understanding their motivation and how to sustain their participation is the key to success in any citizen science project. Studies on participants of citizen science projects illustrate that there is an association between participant motivation and the type of contribution to projects. Thus, in this paper, we define a motivational framework, which classifies participant motivation taking into account the typologies of citizen science projects. Within this framework, we also take into account the importance of motivation in initiating and sustaining participation. This framework helps citizen science practitioners to have comprehensive knowledge about potential motivational factors that can be used to recruit participants, as well as sustaining participation in their projects.

The role of passive surveillance and citizen science in plant health

The early detection of plant pests and diseases is vital to the success of any eradication or control programme, but the resources for surveillance are often limited. Plant health authorities can however make use of observations from individuals and stakeholder groups who are monitoring for signs of ill health. Volunteered data is most often discussed in relation to citizen science groups, however these groups are only part of a wider network of professional agents, land-users and owners who can all contribute to significantly increase surveillance efforts through "passive surveillance". These ad-hoc reports represent chance observations by individuals who may not necessarily be looking for signs of pests and diseases when they are discovered. Passive surveillance contributes vital observations in support of national and international surveillance programs, detecting potentially unknown issues in the wider landscape, beyond points of entry and the plant trade. This review sets out to describe various forms of passive surveillance, identify analytical methods that can be applied to these "messy" unstructured data, and indicate how new programs can be established and maintained. Case studies discuss two tree health projects from Great Britain (TreeAlert and Observatree) to illustrate the challenges and successes of existing passive surveillance programmes. When analysing passive surveillance reports it is important to understand the observers' probability to detect and report each plant health issue, which will vary depending on how distinctive the symptoms are and the experience of the observer. It is also vital to assess how representative the reports are and whether they occur more frequently in certain locations. Methods are increasingly available to predict species distributions from large datasets, but more work is needed to understand how these apply to rare events such as new introductions. One solution for general surveillance is to develop and maintain a network of tree health volunteers, but this requires a large investment in training, feedback and engagement to maintain motivation. There are already many working examples of passive surveillance programmes and the suite of options to interpret the resulting datasets is growing rapidly.

Spatial and Temporal Patterns in Volunteer Data Contribution Activities: A Case Study of eBird

Volunteered geographic information (VGI) has great potential to reveal spatial and temporal dynamics of geographic phenomena. However, a variety of potential biases in VGI are recognized, many of which root from volunteer data contribution activities. Examining patterns in volunteer data contribution activities helps understand the biases. Using eBird as a case study, this study investigates spatial and temporal patterns in data contribution activities of eBird contributors. eBird sampling efforts are biased in space and time. Most sampling efforts are concentrated in areas of denser populations and/or better accessibility, with the most intensively sampled areas being in proximity to big cities in developed regions of the world. Reported bird species are also spatially biased towards areas where more sampling efforts occur. Temporally, eBird sampling efforts and reported bird species are increasing over the years, with significant monthly fluctuations and notably more data reported on weekends. Such trends are driven by the expansion of eBird and characteristics of bird species and observers. The fitness of use of VGI should be assessed in the context of applications by examining spatial, temporal and other biases. Action may need to be taken to account for the biases so that robust inferences can be made from VGI observations.

Citizen scientists and university students monitor noise pollution in cities and protected areas with smartphones

Noise pollution can cause increased stress, cognitive impairment and illness in humans and decreased fitness and altered behavior in wildlife. Maps of noise pollution are used to visualize the distribution of noise across a landscape. These maps are typically created by taking a relatively small number of sound measurements or simulated on the basis of theoretical models. However, smartphones with inexpensive sound measuring apps can be used to monitor noise and create dense maps of real-world noise measurements. Public concern with noise can make monitoring noise pollution with smartphones an engaging and educational citizen science activity. We demonstrate a method utilizing single-day citizen science noise mapping events and a university lab to collect noise data in urban environments and protected areas. Using this approach, we collected hundreds of noise measurements with participants that we used to create noise maps. We found this method was successful in engaging volunteers and students and producing usable noise data. The described methodology has potential applications for biological research, citizen science engagement, and teaching.

Data-derived metrics describing the behaviour of field-based citizen scientists provide insights for project design and modelling bias

Around the world volunteers and non-professionals collect data as part of environmental citizen science projects, collecting wildlife observations, measures of water quality and much more. However, where projects allow flexibility in how, where, and when data are collected there will be variation in the behaviour of participants which results in biases in the datasets collected. We develop a method to quantify this behavioural variation, describing the key drivers and providing a tool to account for biases in models that use these data. We used a suite of metrics to describe the temporal and spatial behaviour of participants, as well as variation in the data they collected. These were applied to 5,268 users of the iRecord Butterflies mobile phone app, a multi-species environmental citizen science project. In contrast to previous studies, after removing transient participants (those active on few days and who contribute few records), we do not find evidence of clustering of participants; instead, participants fall along four continuous axes that describe variation in participants' behaviour: recording intensity, spatial extent, recording potential and rarity recording. Our results support a move away from labelling participants as belonging to one behavioural group or another in favour of placing them along axes of participant behaviour that better represent the continuous variation between individuals. Understanding participant behaviour could support better use of the data, by accounting for biases in the data collection process.

Protection through participation: Crowdsourced tap water quality monitoring for enhanced public health

Lead contamination in municipal drinking water is a national public health issue and is generally the result of water contact with leaded distribution piping and on premise plumbing. As a result, the US Environmental Protection Agency's Lead and Copper Rule requires point of use sampling methods at a small fraction of consumer taps on the public water distribution system. While this approach is practical, it leaves large gaps of consumers without direct monitoring and protection. In response, a novel contest-based crowdsourcing study was conducted to engage the public in monitoring their own water quality at their home taps and study factors that shaped participation in drinking water monitoring. Participants were asked to collect samples of their household drinking water through social media postings, kiosks, and community events with the chance to win a cash prize. The project distributed approximately 800 sampling packets and received 147 packets from participants of which 93% had at least partially completed surveys. On average, private wells were found to have higher lead levels than the public water supply, and the higher lead levels were not attributed to older building age. There is also no statistical relevance between the participants' perceived and actual tap water quality. Survey responses indicated that citizens were motivated to participate in the project due to concerns about their own health and/or the health of their families. In contrast, participants reported that they were not motivated by the cash prize. This project helps inform future public engagement with water quality monitoring, create new knowledge about the influence of personal motivations for participation, and provide recommendations to help increase awareness of water quality issues.

Mapping species richness using opportunistic samples: a case study on ground-floor bryophyte species richness in the Belgian province of Limburg

In species richness studies, citizen-science surveys where participants make individual decisions regarding sampling strategies provide a cost-effective approach to collect a large amount of data. However, it is unclear to what extent the bias inherent to opportunistically collected samples may invalidate our inferences. Here, we compare spatial predictions of forest ground-floor bryophyte species richness in Limburg (Belgium), based on crowd- and expert-sourced data, where the latter are collected by adhering to a rigorous geographical randomisation and data collection protocol. We develop a log-Gaussian Cox process model to analyse the opportunistic sampling process of the crowd-sourced data and assess its sampling bias. We then fit two geostatistical Poisson models to both data-sets and compare the parameter estimates and species richness predictions. We find that the citizens had a higher propensity for locations that were close to their homes and environmentally more valuable. The estimated effects of ecological predictors and spatial species richness predictions differ strongly between the two geostatistical models. Unknown inconsistencies in the sampling process, such as unreported observer’s effort, and the lack of a hypothesis-driven study protocol can lead to the occurrence of multiple sources of sampling bias, making it difficult, if not impossible, to provide reliable inferences.

Towards better species identification processes between scientists and community participants

Urban gardens are a model system for understanding the intersection between biodiversity conservation and citizen science. They contain high plant diversity that contributes to urban flora. However, this diversity is challenging to document due to site access and complex plant taxonomy with hybrids and cultivars. Community research participation provides a tool to measure plant diversity and distribution by engaging gardeners who are most familiar with their plants to report on their garden's species richness using citizen science. Yet there is little empirical exploration of plant identification consistency between citizen scientists and scientific researchers. This could lead to reporting differences (e.g., missing species, multiple reporting of the same species) due to spatial and temporal effects, different perspectives and knowledge systems, and cultural context. We leverage a scientific survey of garden plants and a questionnaire asking gardeners to report on the species in their gardens to perform an opportunistic comparison of gardener and researcher reported plant diversity in community gardens. The comparison shows that gardeners interpret instructions to report plants quite variably, with some reporting all species (including herbaceous weeds) and crop varieties, while others reporting only their main crop species. Scientist on the other hand seek clarity in terms of species and variety and report all species located in the plot, including the small weed species that are overlooked by some gardeners. Consistency could be improved if researchers are more specific about their reporting expectations when asking community members to participate in data collection. We use this case study to communicate that paired citizen scientist-researcher data collection and dialogue between groups is necessary to improve methods for conducting consistent and collaborative assessments of biological diversity.

Hoping for optimality or designing for inclusion: Persistence, learning, and the social network of citizen science

The explosive growth in citizen science combined with a recalcitrance on the part of mainstream science to fully embrace this data collection technique demands a rigorous examination of the factors influencing data quality and project efficacy. Patterns of contributor effort and task performance have been well reviewed in online projects; however, studies of hands-on citizen science are lacking. We used a single hands-on, out-of-doors project-the Coastal Observation and Seabird Survey Team (COASST)-to quantitatively explore the relationships among participant effort, task performance, and social connectedness as a function of the demographic characteristics and interests of participants, placing these results in the context of a meta-analysis of 54 citizen science projects. Although online projects were typified by high (>90%) rates of one-off participation and low retention (<10%) past 1 y, regular COASST participants were highly likely to continue past their first survey (86%), with 54% active 1 y later. Project-wide, task performance was high (88% correct species identifications over the 31,450 carcasses and 163 species found). However, there were distinct demographic differences. Age, birding expertise, and previous citizen science experience had the greatest impact on participant persistence and performance, albeit occasionally in opposite directions. Gender and sociality were relatively inconsequential, although highly gregarious social types, i.e., "nexus people," were extremely influential at recruiting others. Our findings suggest that hands-on citizen science can produce high-quality data especially if participants persist, and that understanding the demographic data of participation could be used to maximize data quality and breadth of participation across the larger societal landscape.

Exposing the Science in Citizen Science: Fitness to Purpose and Intentional Design

Citizen science is a growing phenomenon. With millions of people involved and billions of in-kind dollars contributed annually, this broad extent, fine grain approach to data collection should be garnering enthusiastic support in the mainstream science and higher education communities. However, many academic researchers demonstrate distinct biases against the use of citizen science as a source of rigorous information. To engage the public in scientific research, and the research community in the practice of citizen science, a mutual understanding is needed of accepted quality standards in science, and the corresponding specifics of project design and implementation when working with a broad public base. We define a science-based typology focused on the degree to which projects deliver the type(s) and quality of data/work needed to produce valid scientific outcomes directly useful in science and natural resource management. Where project intent includes direct contribution to science and the public is actively involved either virtually or hands-on, we examine the measures of quality assurance (methods to increase data quality during the design and implementation phases of a project) and quality control (post hoc methods to increase the quality of scientific outcomes). We suggest that high quality science can be produced with massive, largely one-off, participation if data collection is simple and quality control includes algorithm voting, statistical pruning, and/or computational modeling. Small to mid-scale projects engaging participants in repeated, often complex, sampling can advance quality through expert-led training and well-designed materials, and through independent verification. Both approaches-simplification at scale and complexity with care-generate more robust science outcomes.

The distribution and numbers of cheetah (Acinonyx jubatus) in southern Africa

Assessing the numbers and distribution of threatened species is a central challenge in conservation, often made difficult because the species of concern are rare and elusive. For some predators, this may be compounded by their being sparsely distributed over large areas. Such is the case with the cheetah Acinonyx jubatus. The IUCN Red List process solicits comments, is democratic, transparent, widely-used, and has recently assessed the species. Here, we present additional methods to that process and provide quantitative approaches that may afford greater detail and a benchmark against which to compare future assessments. The cheetah poses challenges, but also affords unique opportunities. It is photogenic, allowing the compilation of thousands of crowd-sourced data. It is also persecuted for killing livestock, enabling estimation of local population densities from the numbers persecuted. Documented instances of persecution in areas with known human and livestock density mean that these data can provide an estimate of where the species may or may not occur in areas without observational data. Compilations of extensive telemetry data coupled with nearly 20,000 additional observations from 39 sources show that free-ranging cheetahs were present across approximately 789,700 km2 of Namibia, Botswana, South Africa, and Zimbabwe (56%, 22%, 12% and 10% respectively) from 2010 to 2016, with an estimated adult population of 3,577 animals. We identified a further 742,800 km2 of potential cheetah habitat within the study region with low human and livestock densities, where another ∼3,250 cheetahs may occur. Unlike many previous estimates, we make the data available and provide explicit information on exactly where cheetahs occur, or are unlikely to occur. We stress the value of gathering data from public sources though these data were mostly from well-visited protected areas. There is a contiguous, transboundary population of cheetah in southern Africa, known to be the largest in the world. We suggest that this population is more threatened than believed due to the concentration of about 55% of free-ranging individuals in two ecoregions. This area overlaps with commercial farmland with high persecution risk; adult cheetahs were removed at the rate of 0.3 individuals per 100 km2 per year. Our population estimate for confirmed cheetah presence areas is 11% lower than the IUCN's current assessment for the same region, lending additional support to the recent call for the up-listing of this species from vulnerable to endangered status.

Getting the full picture: Assessing the complementarity of citizen science and agency monitoring data

While the role of citizen science in engaging the public and providing large-scale datasets has been demonstrated, the nature of and potential for this science to supplement environmental monitoring efforts by government agencies has not yet been fully explored. To this end, the present study investigates the complementarity of a citizen science programme to agency monitoring of water quality. The Environment Agency (EA) is the governmental public body responsible for, among other duties, managing and monitoring water quality and water resources in England. FreshWater Watch (FWW) is a global citizen science project that supports community monitoring of freshwater quality. FWW and EA data were assessed for their spatio-temporal complementarity by comparing the geographical and seasonal coverage of nitrate (N-NO₃) sampling across the River Thames catchment by the respective campaigns between spring 2013 and winter 2015. The analysis reveals that FWW citizen science-collected data complements EA data by filling in both gaps in the spatial and temporal coverage as well as gaps in waterbody type and size. In addition, partial spatio-temporal overlap in sampling efforts by the two actors is discovered, but EA sampling is found to be more consistent than FWW sampling. Statistical analyses indicate that regardless of broader geographical overlap in sampling effort, FWW sampling sites are associated with a lower stream order and water bodies of smaller surface areas than EA sampling sites. FWW also samples more still-water body sites than the EA. As a possible result of such differences in sampling tendencies, nitrate concentrations, a measure of water quality, are lower for FWW sites than EA sites. These findings strongly indicate that citizen science has clear potential to complement agency monitoring efforts by generating information on freshwater ecosystems that would otherwise be under reported.