Influence of volunteer and project characteristics on data quality of biological surveys

Volunteer involvement in biological surveys is becoming common in conservation and ecology, prompting questions on the quality of data collected in such surveys. In a systematic review of the peer‐reviewed literature on the quality of data collected by volunteers, we examined the characteristics of volunteers (e.g., age, prior knowledge) and projects (e.g., systematic vs. opportunistic monitoring schemes) that affect data quality with regards to standardization of sampling, accuracy and precision of data collection, spatial and temporal representation of data, and sample size. Most studies (70%, n = 71) focused on the act of data collection. The majority of assessments of volunteer characteristics (58%, n = 93) examined the effect of prior knowledge and experience on quality of the data collected, often by comparing volunteers with experts or professionals, who were usually assumed to collect higher quality data. However, when both groups’ data were compared with the same accuracy standard, professional data were more accurate in only 4 of 7 cases. The few studies that measured precision of volunteer and professional data did not conclusively show that professional data were less variable than volunteer data. To improve data quality, studies recommended changes to survey protocols, volunteer training, statistical analyses, and project structure (e.g., volunteer recruitment and retention).     

Contributions of paraecologists and parataxonomists to research,conservation, and social development

Citizen science has been gaining momentum in the United States and Europe, where citizens are literate and often interested in science. However, in developing countries, which have a dire need for environmental data, such programs are slow to emerge, despite the large and untapped human resources in close proximity to areas of high biodiversity and poorly known floras and faunas. Thus, we propose that the parataxonomist and paraecologist approach, which originates from citizen‐based science, is well suited to rural areas in developing countries. Being a paraecologist or a parataxonomist is a vocation and entails full‐time employment underpinned by extensive training, whereas citizen science involves the temporary engagement of volunteers. Both approaches have their merits depending on the context and objectives of the research. We examined 4 ongoing paraecologist or parataxonomist programs in Costa Rica, India, Papua New Guinea, and southern Africa and compared their origins, long‐term objectives, implementation strategies, activities, key challenges, achievements, and implications for resident communities. The programs supported ongoing research on biodiversity assessment, monitoring, and management, and participants engaged in non‐academic capacity development in these fields. The programs in Southern Africa related to specific projects, whereas the programs in Costa Rica, India, and Papua New Guinea were designed for the long term, provided sufficient funding was available. The main focus of the paraecologists’ and parataxonomists’ activities ranged from collection and processing of specimens (Costa Rica and Papua New Guinea) or of socioeconomic and natural science data (India and Southern Africa) to communication between scientists and residents (India and Southern Africa). As members of both the local land user and research communities, paraecologists and parataxonomists can greatly improve the flow of biodiversity information to all users, from local stakeholders to international academia.     

Using citizen science butterfly counts to predict species population trends

Citizen scientists are increasingly engaged in gathering biodiversity information, but trade‐offs are often required between public engagement goals and reliable data collection. We compared population estimates for 18 widespread butterfly species derived from the first 4 years (2011–2014) of a short‐duration citizen science project (Big Butterfly Count [BBC]) with those from long‐running, standardized monitoring data collected by experienced observers (U.K. Butterfly Monitoring Scheme [UKBMS]). BBC data are gathered during an annual 3‐week period, whereas UKBMS sampling takes place over 6 months each year. An initial comparison with UKBMS data restricted to the 3‐week BBC period revealed that species population changes were significantly correlated between the 2 sources. The short‐duration sampling season rendered BBC counts susceptible to bias caused by interannual phenological variation in the timing of species’ flight periods. The BBC counts were positively related to butterfly phenology and sampling effort. Annual estimates of species abundance and population trends predicted from models including BBC data and weather covariates as a proxy for phenology correlated significantly with those derived from UKBMS data. Overall, citizen science data obtained using a simple sampling protocol produced comparable estimates of butterfly species abundance to data collected through standardized monitoring methods. Although caution is urged in extrapolating from this U.K. study of a small number of common, conspicuous insects, we found that mass‐participation citizen science can simultaneously contribute to public engagement and biodiversity monitoring. Mass‐participation citizen science is not an adequate replacement for standardized biodiversity monitoring but may extend and complement it (e.g., through sampling different land‐use types), as well as serving to reconnect an increasingly urban human population with nature.     

The role of automated feedback in training and retaining biological recorders for citizen science

The rapid rise of citizen science, with lay people forming often extensive biodiversity sensor networks, is seen as a solution to the mismatch between data demand and supply while simultaneously engaging citizens with environmental topics. However, citizen science recording schemes require careful consideration of how to motivate, train, and retain volunteers. We evaluated a novel computing science framework that allowed for the automated generation of feedback to citizen scientists using natural language generation (NLG) technology. We worked with a photo‐based citizen science program in which users also volunteer species identification aided by an online key. Feedback is provided after photo (and identification) submission and is aimed to improve volunteer species identification skills and to enhance volunteer experience and retention. To assess the utility of NLG feedback, we conducted two experiments with novices to assess short‐term (single session) and longer‐term (5 sessions in 2 months) learning, respectively. Participants identified a specimen in a series of photos. One group received only the correct answer after each identification, and the other group received the correct answer and NLG feedback explaining reasons for misidentification and highlighting key features that facilitate correct identification. We then developed an identification training tool with NLG feedback as part of the citizen science program BeeWatch and analyzed learning by users. Finally, we implemented NLG feedback in the live program and evaluated this by randomly allocating all BeeWatch users to treatment groups that received different types of feedback upon identification submission. After 6 months separate surveys were sent out to assess whether views on the citizen science program and its feedback differed among the groups. Identification accuracy and retention of novices were higher for those who received automated feedback than for those who received only confirmation of the correct identification without explanation. The value of NLG feedback in the live program, captured through questionnaires and evaluation of the online photo‐based training tool, likewise showed that the automated generation of informative feedback fostered learning and volunteer engagement and thus paves the way for productive and long‐lived citizen science projects.     

Evaluating environmental education,citizen science,and stewardship through naturalist programs

Amateur naturalists have played an important role in the study and conservation of nature since the 17th century. Today, naturalist groups make important contributions to bridge the gap between conservation science and practice around the world. We examined data from 2 regional naturalist programs to understand participant motivations, barriers, and perspectives as well as the actions they take to advance science, stewardship, and community engagement. These programs provide certification‐based natural history and conservation science training for adults that is followed by volunteer service in citizen science, education, and stewardship. Studies in California and Virginia include quantitative and qualitative evaluation data collected through pre‐ and postcourse surveys, interviews, and long‐term tracking of volunteer hours. Motivations of participants focused on learning about the local environment and plants and animals, connecting with nature, becoming certified, and spending time with people who have similar interests. Over half the participants surveyed were over 50 years old, two‐thirds were women, and a majority reported household incomes of over $50,000 (60% in California, 85% in Virginia), and <20% of those surveyed in both states described themselves as nonwhite. Thus, these programs need to improve participation by a wider spectrum of the public. We interviewed younger and underrepresented adults to examine barriers to participation in citizen science. The primary barrier was lack of time due to the need to work and focus on career advancement. Survey data revealed that participants’ ecological knowledge, scientific skills, and belief in their ability to address environmental issues increased after training. Documented conservation actions taken by the participants include invasive plant management, habitat restoration, and cleanups of natural areas and streams. Long‐term data from Virginia on volunteer hours dedicated to environmental citizen science show an increase from 14% in 2007 to 32% in 2014. In general, participants in the naturalist programs we examined increased their content knowledge about ecosystems, had greater confidence in conserving them, and continued to engage as citizen scientists after completing the program.     

Design principles for engaging and retaining virtual citizen scientists

Citizen science initiatives encourage volunteer participants to collect and interpret data and contribute to formal scientific projects. The growth of virtual citizen science (VCS), facilitated through websites and mobile applications since the mid‐2000s, has been driven by a combination of software innovations and mobile technologies, growing scientific data flows without commensurate increases in resources to handle them, and the desire of internet‐connected participants to contribute to collective outputs. However, the increasing availability of internet‐based activities requires individual VCS projects to compete for the attention of volunteers and promote their long‐term retention. We examined program and platform design principles that might allow VCS initiatives to compete more effectively for volunteers, increase productivity of project participants, and retain contributors over time. We surveyed key personnel engaged in managing a sample of VCS projects to identify the principles and practices they pursued for these purposes and led a team in a heuristic evaluation of volunteer engagement, website or application usability, and participant retention. We received 40 completed survey responses (33% response rate) and completed a heuristic evaluation of 20 VCS program sites. The majority of the VCS programs focused on scientific outcomes, whereas the educational and social benefits of program participation, variables that are consistently ranked as important for volunteer engagement and retention, were incidental. Evaluators indicated usability, across most of the VCS program sites, was higher and less variable than the ratings for participant engagement and retention. In the context of growing competition for the attention of internet volunteers, increased attention to the motivations of virtual citizen scientists may help VCS programs sustain the necessary engagement and retention of their volunteers.     

Assessing citizen contributions to butterfly monitoring in two large cities

Citizen science may be especially effective in urban landscapes due to the large pool of potential volunteers. However, there have been few evaluations of the contributions of citizen scientists to knowledge of biological communities in and around cities. To assess the effectiveness of citizen scientists' monitoring of species in urban areas, we compared butterfly data collected over 10 years in Chicago, Illinois (U.S.A.), and New York City, New York (U.S.A.). The dates, locations, and methods of data collection in Chicago were standardized, whereas data from New York were collected at any location at any time. For each city, we evaluated whether the number of observers, observation days (days on which observations were reported), and sampling locations were associated with the reported proportion of the estimated regional pool of butterfly species. We also compared the number of volunteers, duration of volunteer involvement, and consistency of sampling efforts at individual locations within each city over time. From 2001 to 2010, there were 73 volunteers in Chicago and 89 in New York. During this period, volunteers observed 86% and 89% of the estimated number of butterfly species present in Chicago and New York, respectively. Volunteers in New York reported a greater proportion of the estimated pool of butterfly species per year. In addition, more species were observed per volunteer and observation day in New York, largely due to the unrestricted sampling season in New York. Chicago volunteers were active for more years and monitored individual locations more consistently over time than volunteers in New York. Differences in monitoring protocol--especially length of sampling season and selection protocol for monitoring locations--influenced the relationship between species accrual and sampling effort, which suggests these factors are important in volunteer-based species-monitoring programs.     

Citizen science networks in natural history and the collective validation of biodiversity data

Biodiversity data are in increasing demand to inform policy and management. A substantial portion of these data is generated in citizen science networks. To ensure the quality of biodiversity data, standards and criteria for validation have been put in place. We used interviews and document analysis from the United Kingdom and The Netherlands to examine how data validation serves as a point of connection between the diverse people and practices in natural history citizen science networks. We found that rather than a unidirectional imposition of standards, validation was performed collectively. Specifically, it was enacted in ongoing circulations of biodiversity records between recorders and validators as they jointly negotiated the biodiversity that was observed and the validity of the records. These collective validation practices contributed to the citizen science character or natural history networks and tied these networks together. However, when biodiversity records were included in biodiversity‐information initiatives on different policy levels and scales, the circulation of records diminished. These initiatives took on a more extractive mode of data use. Validation ceased to be collective with important consequences for the natural history networks involved and citizen science more generally.     

Studying citizen science through adaptive management and learning feedbacks as mechanisms for improving conservation

Citizen science has generated a growing interest among scientists and community groups, and citizen science programs have been created specifically for conservation. We examined collaborative science, a highly interactive form of citizen science, which we developed within a theoretically informed framework. In this essay, we focused on 2 aspects of our framework: social learning and adaptive management. Social learning, in contrast to individual‐based learning, stresses collaborative and generative insight making and is well‐suited for adaptive management. Adaptive‐management integrates feedback loops that are informed by what is learned and is guided by iterative decision making. Participants engaged in citizen science are able to add to what they are learning through primary data collection, which can result in the real‐time information that is often necessary for conservation. Our work is particularly timely because research publications consistently report a lack of established frameworks and evaluation plans to address the extent of conservation outcomes in citizen science. To illustrate how our framework supports conservation through citizen science, we examined how 2 programs enacted our collaborative science framework. Further, we inspected preliminary conservation outcomes of our case‐study programs. These programs, despite their recent implementation, are demonstrating promise with regard to positive conservation outcomes. To date, they are independently earning funds to support research, earning buy‐in from local partners to engage in experimentation, and, in the absence of leading scientists, are collecting data to test ideas. We argue that this success is due to citizen scientists being organized around local issues and engaging in iterative, collaborative, and adaptive learning.     

Using citizen science data to identify the sensitivity of species to human land use

Conservation practitioners must contend with an increasing array of threats that affect biodiversity. Citizen scientists can provide timely and expansive information for addressing these threats across large scales, but their data may contain sampling biases. We used randomization procedures to account for possible sampling biases in opportunistically reported citizen science data to identify species’ sensitivities to human land use. We analyzed 21,044 records of 143 native reptile and amphibian species reported to the Carolina Herp Atlas from North Carolina and South Carolina between 1 January 1990 and 12 July 2014. Sensitive species significantly associated with natural landscapes were 3.4 times more likely to be legally protected or treated as of conservation concern by state resource agencies than less sensitive species significantly associated with human‐dominated landscapes. Many of the species significantly associated with natural landscapes occurred primarily in habitats that had been nearly eradicated or otherwise altered in the Carolinas, including isolated wetlands, longleaf pine savannas, and Appalachian forests. Rare species with few reports were more likely to be associated with natural landscapes and 3.2 times more likely to be legally protected or treated as of conservation concern than species with at least 20 reported occurrences. Our results suggest that opportunistically reported citizen science data can be used to identify sensitive species and that species currently restricted primarily to natural landscapes are likely at greatest risk of decline from future losses of natural habitat. Our approach demonstrates the usefulness of citizen science data in prioritizing conservation and in helping practitioners address species declines and extinctions at large extents.     

Conducting conservation social science surveys online

The coronavirus (COVID-19) pandemic is affecting the environment and conservation research in fundamental ways. Many conservation social scientists are now administering survey questionnaires online, but they must do so while ensuring rigor in data collection. Further, they must address a suite of unique challenges, such as the increasing use of mobile devices by participants and avoiding bots or other survey fraud. We reviewed recent literature on online survey methods to examine the state of the field related to online data collection and dissemination. We illustrate the review with examples of key methodological decisions made during a recent national study of people who feed wild birds, in which survey respondents were recruited through an online panel and a sample generated via a project participant list. Conducting surveys online affords new opportunities for participant recruitment, design, and pilot testing. For instance, online survey panels can provide quick access to large and diverse samples of people. Based on the literature review and our own experiences, we suggest that to ensure high-quality online surveys one should account for potential sampling and nonresponse error, design survey instruments for use on multiple devices, test the instrument, and use multiple protocols to identify data quality problems. We also suggest that research funders, journal editors, and policy makers can all play a role in ensuring high-quality survey data are used to inform effective conservation programs and policies.     

A framework for evaluating and designing citizen science programs for natural resources monitoring

We present a framework of resource characteristics critical to the design and assessment of citizen science programs that monitor natural resources. To develop the framework we reviewed 52 citizen science programs that monitored a wide range of resources and provided insights into what resource characteristics are most conducive to developing citizen science programs and how resource characteristics may constrain the use or growth of these programs. We focused on 4 types of resource characteristics: biophysical and geographical, management and monitoring, public awareness and knowledge, and social and cultural characteristics. We applied the framework to 2 programs, the Tucson (U.S.A.) Bird Count and the Maui (U.S.A.) Great Whale Count. We found that resource characteristics such as accessibility, diverse institutional involvement in resource management, and social or cultural importance of the resource affected program endurance and success. However, the relative influence of each characteristic was in turn affected by goals of the citizen science programs. Although the goals of public engagement and education sometimes complimented the goal of collecting reliable data, in many cases trade‐offs must be made between these 2 goals. Program goals and priorities ultimately dictate the design of citizen science programs, but for a program to endure and successfully meet its goals, program managers must consider the diverse ways that the nature of the resource being monitored influences public participation in monitoring.     

The value, limitations, and challenges of employing local experts in conservation research

Abstract: Evidence suggests that the involvement of local people in conservation work increases a project's chances of success. Involving citizen scientists in research, however, raises questions about data quality. As a tool to better assess potential participants for conservation projects, we developed a knowledge gradient, K, along which community members occupy different positions on the basis of their experience with and knowledge of a research subject. This gradient can be used to refine the citizen–science concept and allow researchers to differentiate between community members with expert knowledge and those with little knowledge. We propose that work would benefit from the inclusion of select local experts because it would allow researchers to harness the benefits of local involvement while maintaining or improving data quality. We used a case study from the DeHoop Nature Preserve, South Africa, in which we conducted multiple interviews, identified and employed a local expert animal tracker, evaluated the expert's knowledge, and analyzed the data collected by the expert. The expert animal tracker J.J. created his own sampling design and gathered data on mammals. He patrolled 4653 km in 214 days and recorded 4684 mammals. He worked from a central location, and his patrols formed overlapping loops; however, his data proved neither spatially nor temporally autocorrelated. The distinctive data collected by J.J. are consistent with the notion that involving local experts can produce reliable data. We developed a conceptual model to help identify the appropriate participants for a given project on the basis of research budget, knowledge or skills needed, technical literacy requirements, and scope of the project.     

Conservation and monitoring of a persecuted African lion population by Maasai warriors

Although Africa has many threatened species and biological hot spots, there are few citizen science schemes, particularly in rural communities, and there has been limited evaluation of existing programs. We engaged traditional Maasai warriors (pastoralist men aged 15 to 35) in community‐based conservation and demographic monitoring of a persecuted African lion (Panthera leo) population. Through direct engagement, we investigated whether a citizen science approach employing local warriors, who had no formal education, could produce reliable data on the demographics, predation, and movements of a species with which their communities have been in conflict for generations. Warriors were given benefits such as literacy training and skill enhancement and engaged in the monitoring of the lions. The trained warriors reported on lion sign across an area nearly 4000 km². Scientists worked together with the warriors to verify their reports and gather observations on the lion population. Using the verified reports and collected observations, we examined our scientific knowledge relative to the lion population preceding and during the citizen science program. Our observations showed that data quality and quantity improved with the involvement and training of the participants. Furthermore, because they engaged in conservation and gained personal benefits, the participants came to appreciate a species that was traditionally their foe. We believe engaging other local communities in biodiversity conservation and monitoring may be an effective conservation approach in rural Africa.     

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

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

Evaluating the Quality of Citizen‐Scientist Data on Pollinator Communities

Abstract: Concerns about pollinator declines have grown in recent years, yet the ability to detect changes in abundance, taxonomic richness, and composition of pollinator communities is hampered severely by the lack of data over space and time. Citizen scientists may be able to extend the spatial and temporal extent of pollinator monitoring programs. We developed a citizen‐science monitoring protocol in which we trained 13 citizen scientists to observe and classify floral visitors at the resolution of orders or super families (e.g., bee, wasp, fly) and at finer resolution within bees (superfamily Apoidea) only. We evaluated the protocol by comparing data collected simultaneously at 17 sites by citizen scientists (observational data set) and by professionals (specimen‐based data set). The sites differed with respect to the presence and age of hedgerows planted to improve habitat quality for pollinators. We found significant, positive correlations among the two data sets for higher level taxonomic composition, honey bee (Apis mellifera) abundance, non‐Apis bee abundance, bee richness, and bee community similarity. Results for both data sets also showed similar trends (or lack thereof) in these metrics among sites differing in the presence and age of hedgerows. Nevertheless, citizen scientists did not observe approximately half of the bee groups collected by professional scientists at the same sites. Thus, the utility of citizen‐science observational data may be restricted to detection of community‐level changes in abundance, richness, or similarity over space and time, and citizen‐science observations may not reliably reflect the abundance or frequency of occurrence of specific pollinator species or groups.     

Expert variability provides perspective on the strengths and weaknesses of citizen-driven intertidal monitoring program

Citizen scientist programs are a means to efficiently conduct large-scale surveys of ecosystems or managed species, provided that concerns over the quality and use of data generated by nonexperts can be addressed. This study presents actions taken in a citizen science program to assure data quality and demonstrates the validity of citizen-generated data. In this case the accuracy of data collected by secondary school students as citizens in a program that quantitatively sampled benthic rocky intertidal communities at 13 sites on Maui, Molokai, Oahu, and Hawai'i island during the years 2004-2007 was evaluated. In 2007, two independent research teams collected data simultaneously with students at five sites on eight sampling dates. Comparisons of Shannon diversity and Bray-Curtis similarity values computed and simulated from student and researcher collected data revealed that nonexpert students accurately collect community-level data within the range of the variation that occurs between researchers. Students were, however, likely to misidentify cryptic and rare species. These findings have direct implications for the conservation goals of the monitoring program as the assessment reveals that students are likely to misidentify early alien introductions but are able to monitor the abundances of native and introduced species once they become established. The validity assessment designed for this investigation is unique in that it directly compares consistent errors made by citizens in data collection to expert variability to identify usage limitations and can be a guide for future studies that involve the efforts of trained volunteers.     

Mapping epistemic cultures and learning potential of participants in citizen science projects

The ever‐widening scope and range of global change and interconnected systemic risks arising from people–environment relationships (social‐ecological risks) appears to be increasing concern among, and involvement of, citizens in an increasingly diversified number of citizen science projects responding to these risks. We examined the relationship between epistemic cultures in citizen science projects and learning potential related to matters of concern. We then developed a typology of purposes and a citizen science epistemic‐cultures heuristic and mapped 56 projects in southern Africa using this framework. The purpose typology represents the range of knowledge‐production purposes, ranging from laboratory science to social learning, whereas the epistemic‐cultures typology is a relational representation of scientist and citizen participation and their approach to knowledge production. Results showed an iterative relationship between matters of fact and matters of concern across the projects; the nexus of citizens’ engagement in knowledge‐production activities varied. The knowledge‐production purposes informed and shaped the epistemic cultures of all the sampled citizen science projects, which in turn influenced the potential for learning within each project. Through a historical review of 3 phases in a long‐term river health‐monitoring project, we found that it is possible to evolve the learning curve of citizen science projects. This evolution involved the development of scientific water monitoring tools, the parallel development of pedagogic practices supporting monitoring activities, and situated engagement around matters of concern within social activism leading to learning‐led change. We conclude that such evolutionary processes serve to increase potential for learning and are necessary if citizen science is to contribute to wider restructuring of the epistemic culture of science under conditions of expanding social‐ecological risk.     

Site‐Occupancy Distribution Modeling to Correct Population‐Trend Estimates Derived from Opportunistic Observations

Abstract: Species’ assessments must frequently be derived from opportunistic observations made by volunteers (i.e., citizen scientists). Interpretation of the resulting data to estimate population trends is plagued with problems, including teasing apart genuine population trends from variations in observation effort. We devised a way to correct for annual variation in effort when estimating trends in occupancy (species distribution) from faunal or floral databases of opportunistic observations. First, for all surveyed sites, detection histories (i.e., strings of detection–nondetection records) are generated. Within‐season replicate surveys provide information on the detectability of an occupied site. Detectability directly represents observation effort; hence, estimating detectablity means correcting for observation effort. Second, site‐occupancy models are applied directly to the detection‐history data set (i.e., without aggregation by site and year) to estimate detectability and species distribution (occupancy, i.e., the true proportion of sites where a species occurs). Site‐occupancy models also provide unbiased estimators of components of distributional change (i.e., colonization and extinction rates). We illustrate our method with data from a large citizen‐science project in Switzerland in which field ornithologists record opportunistic observations. We analyzed data collected on four species: the widespread Kingfisher (Alcedo atthis) and Sparrowhawk (Accipiter nisus) and the scarce Rock Thrush (Monticola saxatilis) and Wallcreeper (Tichodroma muraria). Our method requires that all observed species are recorded. Detectability was <1 and varied over the years. Simulations suggested some robustness, but we advocate recording complete species lists (checklists), rather than recording individual records of single species. The representation of observation effort with its effect on detectability provides a solution to the problem of differences in effort encountered when extracting trend information from haphazard observations. We expect our method is widely applicable for global biodiversity monitoring and modeling of species distributions.