Citizen Science Monitoring Research Articles

C30 — A simple, rapid, scientifically valid, and low-cost method for citizen-scientists to monitor coral reefs

The extent and speed of recent changes in reef coral abundances due to ocean warming and human impacts require more widespread capability to map and measure these changes, especially in countries like the Philippines. We present “C30”, a simple, rapid, scientifically valid, and low-cost method for skin divers or freedivers to take random photo-quadrat images within predefined stations on the upper reef slope. C30 yields coral cover data similar to those collected using the more intensive C5 method and can be as powerful in detecting small differences in reef cover. Less time is also needed for training personnel and sampling. However, more photo-quadrat images, better cameras, and closer collaboration with local scientists are required if higher precision data and estimates of coral diversity are needed from C30. C30 is a valuable tool for participatory, community-based citizen science monitoring of coral reefs.

Merging science education, citizen science and general population noise exposure data collection in the battle against noise pollution

Science, Technology, Engineering, Mathematics in education is commonly referred to as STEM. The last decades illustrate that our society is transferring into an ever accelerating technological environment. In parallel, the general public became an important driving force in collecting citizen science data to trigger legislative pressure and impact on policy makers to accelerate the improvement of their quality of life. That practice is currently extending into the environmental impact of noise related quality of life. This publication suggests to merge those educational STEM goals, citizen science monitoring and the need for population based noise monitoring data for efficient policy support. The presented educational project can be regarded as a proof-of-concept and can be repeated in different schools and classes every year. This approach has the potential to acquire abundant noise monitoring data and provides an unbiased population sampling dataset by design. This population driven involvement allows to assess real-life and long-term noise policy impact and could become a fundamental pillar in achieving the overall societal goal of improving noise related environmental quality of life.

Elucidating the community health impacts of odours using citizen science and mobile monitoring

Elucidating the community health impacts of odours using citizen science and mobile monitoring

Imputation of incomplete large‐scale monitoring count data via penalized estimation

Abstract In biodiversity monitoring, large datasets are becoming more and more widely available and are increasingly used globally to estimate species trends and conservation status. These large‐scale datasets challenge existing statistical analysis methods, many of which are not adapted to their size, incompleteness and heterogeneity. The development of scalable methods to impute missing data in incomplete large‐scale monitoring datasets is crucial to balance sampling in time or space and thus better inform conservation policies. We developed a new method based on penalized Poisson models to impute and analyse incomplete monitoring data in a large‐scale framework. The method allows parameterization of (a) space and time factors, (b) the main effects of predictor covariates, as well as (c) space–time interactions. It also benefits from robust statistical and computational capability in large‐scale settings. The method was tested extensively on both simulated and real‐life waterbird data, with the findings revealing that it outperforms six existing methods in terms of missing data imputation errors. Applying the method to 16 waterbird species, we estimated their long‐term trends for the first time at the entire North African scale, a region where monitoring data suffer from many gaps in space and time series. This new approach opens promising perspectives to increase the accuracy of species‐abundance trend estimations. We made it freely available in the r package ‘lori’ (https://CRAN.R‐project.org/package=lori) and recommend its use for large‐scale count data, particularly in citizen science monitoring programmes.

How citizen science could improve species distribution models and their independent assessment.

Species distribution models (SDM) have been increasingly developed in recent years, but their validity is questioned. Their assessment can be improved by the use of independent data, but this can be difficult to obtain and prohibitive to collect. Standardized data from citizen science may be used to establish external evaluation datasets and to improve SDM validation and applicability.We used opportunistic presence‐only data along with presence–absence data from a standardized citizen science program to establish and assess habitat suitability maps for 9 species of amphibian in western France. We assessed Generalized Additive and Random Forest Models’ performance by (1) cross‐validation using 30% of the opportunistic dataset used to calibrate the model or (2) external validation using different independent datasets derived from citizen science monitoring. We tested the effects of applying different combinations of filters to the citizen data and of complementing it with additional standardized fieldwork.Cross‐validation with an internal evaluation dataset resulted in higher AUC (Area Under the receiver operating Curve) than external evaluation causing overestimation of model accuracy and did not select the same models; models integrating sampling effort performed better with external validation. AUC, specificity, and sensitivity of models calculated with different filtered external datasets differed for some species. However, for most species, complementary fieldwork was not necessary to obtain coherent results, as long as the citizen science data were strongly filtered.Since external validation methods using independent data are considered more robust, filtering data from citizen sciences may make a valuable contribution to the assessment of SDM. Limited complementary fieldwork with volunteer's participation to complete ecological gradients may also possibly enhance citizen involvement and lead to better use of SDM in decision processes for nature conservation.

The invisible enemy: Understanding bird‐window strikes through citizen science in a focal city

AbstractBird‐window collisions have been estimated to be among the most important sources of bird death. Despite increasing knowledge in Latin America, our understanding of this phenomenon is still incipient, with research performed in Mexico limited to a handful of studies. Here, we present the results of a citizen science effort focused on bird‐window collisions at seven buildings in the university campus of the National School of Higher Studies (ENES) of the National Autonomous University of Mexico, located in the city of León (central Mexico). Our main goal was to describe seasonal patterns of bird‐window collisions and their relationship with building traits (i.e., building height, window area) through citizen science monitoring strategies. Our results showed that collisions were higher in two of the seven studied buildings, with two bird species recording almost half of the total collisions: Clay‐colored Sparrow (Spizella pallida) and Indigo Bunting (Passerina cyanea). Seasonally, April was the only month to differ from the rest of the studied months, showing significantly higher rate of bird‐window collision. Regarding building traits, only building height was related to the number of recorded bird‐window collisions. In sum, our study provides findings from an understudied area, showing the value of citizen science approaches to generate knowledge on a deadly phenomenon. Notably, besides the potential drawbacks and importance of generating this kind of information, our project raised awareness on the topic across the entire campus community, from the students and academics to the administration, highlighting the potential for social impact with these kinds of projects.

Using Critical Environmental Agency to Engage Teachers in Local Watersheds through Water Quality Citizen Science

This qualitative study explores teachers’ critical environmental agency (CEA) through deepening content knowledge, engaging in identity development, developing a critical consciousness of place, and moving toward civic action. We explored the meanings secondary science teachers made of an on-going professional development (PD) situated in the Okefenokee Swamp (unique ecosystem that drains to Gulf of Mexico and Atlantic Ocean) and focused on local watershed citizen science monitoring and the global implications of all water being connected. Data analyses focused on how the nineteen teachers’ experiences and meanings were leveraged to develop CEA and the constraints that restricted their CEA development. Our findings broaden the understanding of how teachers, who teach historically underrepresented youth in low socioeconomic rural areas, come to see themselves as people who care about the environment and become empowered to envision a more sustainable future for their students and communities.

Citizen Science Monitoring for Sustainable Development Goal Indicator 6.3.2 in England and Zambia

Citizen science has the potential to support the delivery of the United Nations Sustainable Development Goals (SDGs) through its integration into national monitoring schemes. In this study, we explored the opportunities and biases of citizen science (CS) data when used either as a primary or secondary source for SDG 6.3.2 reporting. We used data from waterbodies with both CS and regulatory monitoring in England and Zambia to explore their biases and complementarity. A comparative analysis of regulatory and CS data provided key information on appropriate sampling frequency, site selection, and measurement parameters necessary for robust SDG reporting. The results showed elevated agreement for pass/fail ratios and indicator scores for English waterbodies (80%) and demonstrated that CS data improved for granularity and spatial coverage for SDG indicator scoring, even when extensive statutory monitoring programs were present. In Zambia, management authorities are actively using citizen science projects to increase spatial and temporal coverage for SDG reporting. Our results indicate that design considerations for SDG focused citizen science can address local needs and provide a more representative indicator of the state of a nation’s freshwater ecosystems for international reporting requirements.

A Hybrid Method for Citizen Science Monitoring of Recreational Trampling in Urban Remnants: A Case Study from Perth, Western Australia

Vegetation trampling that arises from off-trail excursions by people walking for recreation can negatively impact the structure of understory plants in natural spaces that are an essential element of urban green infrastructure in a modern city. In addition to reducing the esthetic quality and environmental values of urban remnant and replanted native vegetation, such trampling reduces the habitat that supports wildlife populations within the urban fabric. This case study draws upon several disparate methods for measuring vegetation structure and trampling impacts to produce a hybrid method that community-based citizen scientists (and land managers and other researchers) could use to simply, rapidly, and reproducibly monitor how trampling associated with urban recreation trails impacts the structure of understory vegetation. Applying the novel hybrid method provided evidence that trampling had reduced the vegetation structure adjacent to a recreational walking trail in an urban woodland remnant in Perth, Western Australia. The hybrid method also detected ecological variability at the local ecosystem-scale at a second similar woodland remnant in Perth. The hybrid sampling method utilized in this case study provides an effective, efficient, and reproducible data collection method that can be applied to recreation ecology research into aspects of trampling associated with trail infrastructure.

The Role of Citizen Science in Meeting SDG Targets around Soil Health

Healthy soils are vital for sustainable development, yet consistent soil monitoring is scarce, and soils are poorly represented in United Nations Sustainable Development Goals targets and indicators. There is a clear need for specific ambitions on soil health, accompanying metrics, and cost-effective monitoring methodologies. In this paper, we review citizen science methods and platforms which could compliment structured soil monitoring programmes and contribute to filling this knowledge gap. We focussed on soil structure, organic carbon, biodiversity, nutrients, and vegetation cover. Each method was classified as red, amber, or green (RAG) in terms of time requirements, cost, and data reliability. Toolkits were assessed in terms of cost and requirement for specialist kit. We found 32 methods across the five indicators. Three soil monitoring methods scored green on all criteria, and 20 (63%) scored green on two criteria. We found 13 toolkits appropriate for citizen science monitoring of soil health. Three of them are free, easy to use, and do not require specialist equipment. Our review revealed multiple citizen science methods and toolkits for each of the five soil health indicators. This should pave the way towards a cost-effective, joined-up approach on soil health, informing national and international policy and supporting the move towards farmer-led, data-driven decision-making.

Koala Counter: Recording Citizen Scientists’ search paths to Improve Data Quality

Biodiversity monitoring is key for developing informed solutions to the threats facing our environment, including habitat loss, increasing pollution, wildlife trafficking and climate change. Citizen science is increasingly used for collecting species observational data at wide spatial and temporal scales that are difficult and expensive to achieve using traditional means. Current apps used for citizen science biodiversity monitoring provide methods to record observational data on species’ presence, including photos, location, date, time and an assortment of other data. However, data about species absences as well as automatically generated and accurate data on both search effort and search locations have been lacking. Koala Counter is a free, cross-platform (Android & iOS), open-source app that was developed for a citizen science project to collect koala population data to inform koala conservation and management in South Australia. The app uses mobile phone sensors to transparently and automatically record metadata such as species observation location and time, the search path the user takes, the time taken while searching and GPS location precision. We tested this in the Citizen Science event “The Great Koala Count 2” in South Australia during November 2016. Observations, paths and search effort data were accurate overall. Location accuracy was good, with some exceptions. Use of the app indicated a number of potential improvements that would further increase data quality. Recording search paths offers a potentially valuable method of recording spatial and temporal components of search effort, improving on simple records of species observations and time taken, especially when no observations are made. These data may enable better ecological modelling by supplying accurate search effort data as well as enabling improved inference of species absence. Search paths also show locations that have not been searched, which is valuable information in management of citizen science monitoring programs.

Changes in flight period predict trends in abundance of Massachusetts butterflies.

Phenological shifts are well-documented in the ecological literature. However, their significance for changes in demography and abundance is less clear. We used 27years of citizen science monitoring to quantify trends in phenology and relative abundance across 89 butterfly species. We calculated shifts in phenology using quantile regression and shifts in relative abundance using list length analysis and counts from field trips. Elongated activity periods within a year were the strongest predictor of increases in relative abundance. These changes may be driven in part by changes in voltinism, as this association was stronger in multivoltine species. Some species appear to be adding a late-season generation, whereas other species appear to be adding a spring generation, revealing a possible shift from vagrant to resident. Our results emphasise the importance of evaluating phenological changes throughout species' flight period and understanding the consequences for such climate-related changes on viability or population dynamics.

Artificial night light helps account for observer bias in citizen science monitoring of an expanding large mammal population.

The integration of citizen scientists into ecological research is transforming how, where, and when data are collected, and expanding the potential scales of ecological studies. Citizen-science projects can provide numerous benefits for participants while educating and connecting professionals with lay audiences, potentially increasing the acceptance of conservation and management actions. However, for all the benefits, collection of citizen-science data is often biased towards areas that are easily accessible (e.g. developments and roadways), and thus data are usually affected by issues typical of opportunistic surveys (e.g. uneven sampling effort). These areas are usually illuminated by artificial light at night (ALAN), a dynamic sensory stimulus that alters the perceptual world for both humans and wildlife. Our goal was to test whether satellite-based measures of ALAN could improve our understanding of the detection process of citizen-scientist-reported sightings of a large mammal. We collected observations of American black bears Ursus americanus (n=1,315) outside their primary range in Minnesota, USA, as part of a study to gauge population expansion. Participants from the public provided sighting locations of bears on a website. We used an occupancy modelling framework to determine how well ALAN accounted for observer metrics compared to other commonly used metrics (e.g. housing density). Citizen scientists reported 17% of bear sightings were under artificially lit conditions and monthly ALAN estimates did the best job accounting for spatial bias in detection of all observations, based on AIC values and effect sizes ( =0.81, 0.71-0.90 95% CI). Bear detection increased with elevated illuminance; relative abundance was positively associated with natural cover, proximity to primary bear range and lower road density. Although the highest counts of bear sightings occurred in the highly illuminated suburbs of the Minneapolis-St. Paul metropolitan region, we estimated substantially higher bear abundance in another region with plentiful natural cover and low ALAN (up to ~375% increased predicted relative abundance) where observations were sparse. We demonstrate the importance of considering ALAN radiance when analysing citizen-scientist-collected data, and we highlight the ways that ALAN data provide a dynamic snapshot of human activity.

Phenological mismatch between breeding birds and their surveyors and implications for estimating population trends

Citizen science monitoring of common birds often involves volunteers visiting selected survey locations twice per year, with interannual differences in the number of birds detected used to infer population trends. Two processes, changes in the timing of migration and breeding in response to climate change, and changes in the timing of surveys by volunteers, may cause variation in the number of birds detected, leading to biases in inferred population trends. We assessed the magnitude of potential biases using the UK Breeding Bird Survey, comparing survey timing, species phenologies and apparent trend biases between 1994–98 and 2013–17. To control for large-scale geographic effects, we focussed on a subset of 888 surveyed 1 km squares in South-East England. Survey dates became significantly earlier, advancing by 2–4 days on average. We calculated seasonal patterns of bird abundance for 68 species. After standardising these to remove long-term abundance trends, median detection dates were advanced by 0.82 days on average. At the species level, the majority of changes were ± 2 days and only five species showed a significant advancement in median detection date. However, species’ phenological changes alone are capable of inducing between an 8% suppression and 21% enhancement of species’ trends, although the majority are ± 2%. Effects of a similar magnitude are apparent if survey timings are also allowed to change, although different species are affected. Small modifications to the statistical model used to generate trends can control for changes in survey timing, but without additional survey visits, or using data from other sources, we cannot currently control for seasonal variation in detectability. Although the average effects shown here are small, biases could become increasingly important for some species, and we recommend organisers of biodiversity monitoring schemes assess whether their methods are resistant to variations in species phenology and survey timing.

Observer retention, site selection and population dynamics interact to bias abundance trends in bats

Abstract Many long‐term wildlife population monitoring programmes rely on citizen scientists for data collection. This can offer several benefits over traditional monitoring practices as it is a cost‐effective, large‐scale approach capable of providing long time series data and raising public environmental awareness. Whilst there is a debate about the quality of citizen science data, a standardised sampling design can allow citizen science data to be of a similar quality to those collected by professionals. However, many programmes use subjective, opportunistic selection of monitoring sites and this introduces several types of bias, which are not well understood. Using bat roost counts as a case study, we took a ‘virtual ecologist’ approach to simulate the effect of opportunistic site selection and uneven observer retention on our ability to accurately detect abundance trends. We simulated populations with different levels of temporal variability and site fidelity. Our simulations reveal that opportunistic site selection and low observer retention can result in biased trends and that these biases are magnified when monitored populations exhibit high levels of inter‐annual variation and low site fidelity. These results show that the synergistic effects of observer behaviour, site selection, and population dynamics lead to biased abundance trends in monitoring programmes. This study highlights the value of engaging and retaining citizen science observers, a standardised sampling design, and the collection of metadata. We conclude that monitoring programmes need to be aware of their focal species' temporal variability and site fidelity to adequately assess the potential bias caused by opportunistic site selection and low observer retention. Synthesis and applications. Accurate data on population changes are key for conservation success. Therefore, it is important that citizen science monitoring programmes assess and potentially quantify the biases present in their data. We demonstrate the applicability of an established simulation framework to assess the effect of biases on our ability to correctly detect abundance trends. Our findings highlight that monitoring programmes need to be aware of their study species’ temporal variability and site fidelity to assess and account for the effects of biased site selection and observer retention.

Dispersal patterns of an introduced wild bee, Megachile sculpturalis Smith, 1853 (Hymenoptera: Megachilidae) in European alpine countries.

Biodiversity monitoring programs are the baseline of species abundancy studies, which in case of introduced species are especially critical. Megachile sculpturalis Smith, 1853 native to Eastern-Asia, constitutes the first ever recorded wild bee species accidently introduced in Europe. Since its first discovery in 2008, M. sculpturalis has been spreading across the continent. By initiating a citizen science monitoring program, we aimed to investigate the occurrence pattern of M. sculpturalis. Within only two years after starting the project, 111 new reports from Switzerland, Liechtenstein and Austria were recorded. Comparably to other European countries, the population progressed remarkably fast from year to year expanding its area geographically but also ecologically by increasing its altitudinal range. The distribution pattern indicates human assisted jump-dispersal travelling on the major traffic routes of central Europe.

Integrating citizen science data with expert surveys increases accuracy and spatial extent of species distribution models

AbstractAimInformation on species’ habitat associations and distributions, across a wide range of spatial and temporal scales, is a fundamental source of ecological knowledge. However, collecting information at relevant scales is often cost prohibitive, although it is essential for framing the broader context of more focused research and conservation efforts. Citizen science has been signalled as an increasingly important source to fill in data gaps where information is needed to make comprehensive and robust inferences on species distributions. However, there are perceived trade‐offs of combining highly structured, scientific survey data with largely un‐structured, citizen science data.MethodsWe explore these trade‐offs by applying a simplified approach of filtering citizen science data to resemble structured survey data and analyse both sources of data under a common framework. To accomplish this, we integrated high‐resolution survey data on shorebirds in the northern Central Valley of California with observations in eBird for the entire region that were filtered to improve their quality.ResultsThe integration of survey data with the filtered citizen science data resulted in improved inference and increased the extent and accuracy of distribution models on shorebirds for the Central Valley. The structured surveys improved the overall accuracy of ecological inference over models using citizen science data only by increasing the representation of data collected from high‐quality habitats for shorebirds.Main conclusionsThe practical approach we have shown for data integration can also be used to improve the efficiency of designing biological surveys in the context of larger, citizen science monitoring efforts, ultimately reducing the financial and time expenditures typically required of monitoring programs and focused research. The simple method we present can be used to integrate other types of data with more localized efforts, ultimately improving our ecological knowledge on the distribution and habitat associations of species of conservation concern worldwide.

Estimating the spatial coverage of citizen science for monitoring threatened species

Monitoring threatened species is vital for effective conservation, and citizen science can fill information gaps where professionally derived monitoring data are unavailable or guide where further survey efforts may be warranted. Yet the geographic and taxonomic coverage of citizen science projects is poorly understood. Using a snapshot in time approach, we reviewed citizen science monitoring and survey projects in Australia in 2017 and identified 133 projects contributing to threatened species monitoring or conservation action in both terrestrial and marine environments. Most projects (61%) are relevant for 10 or fewer threatened species. Relevant citizen science projects tend to be concentrated along the more densely populated eastern and south-western coasts, while relatively few projects occur in northern regions of Australia. Our findings show a high convergence between citizen science project densities and threatened species richness in many terrestrial areas, although they also highlight areas with potential to expand citizen science, and indicate areas where professional monitoring is unlikely to be augmented by citizen science.

Application of the Random Encounter Model in citizen science projects to monitor animal densities

AbstractAbundance and density are vital metrics for assessing a species’ conservation status and for developing effective management strategies. Remote‐sensing cameras are being used increasingly as part of citizen science projects to monitor wildlife, but current methodologies to monitor densities pose challenges when animals are not individually recognizable. We investigated the use of camera traps and the Random Encounter Model (REM) for estimating the density of West European hedgehogs (Erinaceus europaeus) within a citizen science framework. We evaluated the use of a simplified version of theREMin terms of the parameters’ estimation (averaged vs. survey‐specific) and assessed its potential application as part of a large‐scale, long‐term citizen science project. We compared averagedREMestimates to those obtained via spatial capture–recapture (SCR) using data from nocturnal spotlight surveys. There was a high degree of concordance inREM‐derived density estimates from averaged parameters versus those derived from survey‐specific parameters. AveragedREMdensity estimates were also comparable to those produced bySCRat eight out of nine sites; hedgehog density was 7.5 times higher in urban (32.3 km−2) versus rural (4.3 km2) sites. Power analyses indicated that the averagedREMapproach would be able to detect a 25% change in hedgehog density in both habitats with >90% power. Furthermore, despite the high start‐up costs associated with theREMmethod, it would be cost‐effective in the long term. The averagedREMapproach is a promising solution to the challenge of large‐scale and long‐term species monitoring. We suggest including theREMas part of a citizen science monitoring project, where participants collect data and researchers verify and implement the required analysis.

Accuracy of long-term volunteer water monitoring data: A multiscale analysis from a statewide citizen science program.

An increasing number of citizen science water monitoring programs is continuously collecting water quality data on streams throughout the United States. Operating under quality assurance protocols, this type of monitoring data can be extremely valuable for scientists and professional agencies, but in some cases has been of limited use due to concerns about the accuracy of data collected by volunteers. Although a growing body of studies attempts to address accuracy concerns by comparing volunteer data to professional data, rarely has this been conducted with large-scale datasets generated by citizen scientists. This study assesses the relative accuracy of volunteer water quality data collected by the Texas Stream Team (TST) citizen science program from 1992-2016 across the State of Texas by comparing it to professional data from corresponding stations during the same time period. Use of existing data meant that sampling times and protocols were not controlled for, thus professional and volunteer comparisons were refined to samples collected at stations within 60 meters of one another and during the same year. Results from the statewide TST dataset include 82 separate station/year ANOVAs and demonstrate that large-scale, existing volunteer and professional data with unpaired samples can show agreement of ~80% for all analyzed parameters (DO = 77%, pH = 79%, conductivity = 85%). In addition, to assess whether limiting variation within the source datasets increased the level of agreement between volunteers and professionals, data were analyzed at a local scale. Data from a single partner city, with increased controls on sampling times and locations and correction of a systematic bias in DO, confirmed this by showing an even greater agreement of 91% overall from 2009-2017 (DO = 91%, pH = 83%, conductivity = 100%). An experimental sampling dataset was analyzed and yielded similar results, indicating that existing datasets can be as accurate as experimental datasets designed with researcher supervision. Our findings underscore the reliability of large-scale citizen science monitoring datasets already in existence, and their potential value to scientific research and water management programs.