Integrating historical records and citizen science data to understand bird responses to climate change in Concord, Massachusetts: Thoreau to eBird

The Wallacean deficit continues to be a challenge to species distribution modelling. Although some authors have suggested that data collected by citizen scientists can be relevant for a better understanding of biodiversity, to our knowledge, no work has quantitatively tested the equivalence between scientific and citizen science data. Here, we investigate the hypothesis that data collected by citizen scientists can be equivalent to data collected by professional scientists when generating species spatial distribution models. For 42 bird species in the Cerrado region we generated and compared species distribution models based on three data sources: (1) scientific data, (2) citizen science data and (3) sample size corrected citizen science data. To test our hypothesis, we compared the equivalence of these datasets. We rejected the hypothesis of equivalence for about one‐third (38%) of the evaluated species, revealing that, for most of the species considered, the models generated were equivalent irrespective of the data set used. The distances between centroids of the models that were equivalent were on average smaller than the distances between non‐equivalent models. Also, the direction of change in the models showed no pattern, with no trend towards more populated regions. Our results show that the use of data collected by citizen scientists can be an ally in filling the Wallacean deficit gap. In fact, the lack of use of this wide range of data collected by citizen scientists seems to be an unjustified caution. We indicate the potential of using citizen science data for modelling the distribution of species, mainly due to the large set of data collected, which is impracticable for scientists alone to collect. Conservation measures will be favoured by the union of professional and amateur data, aiming for a better understanding of species distribution and, consequently, biodiversity conservation.

Minimizing Data Waste: Conservation in the Big Data Era

Invasive Alien Species (IAS) are a growing threat to Europe's biodiversity. The implementation of European Union Regulation on IAS can benefit from the involvement of the public in IAS recording and management through Citizen Science (CS) initiatives. Aiming to tackle issues related with the use of CS projects on IAS topics, a dedicated workshop titled “Citizen Science and Open Data: a model for Invasive Alien Species in Europe” was organized by the Joint Research Centre (JRC) and the European Cooperation in Science and Technology (COST Association). Fifty key stakeholders from all Europe, including two Members of the European Parliament, attended the workshop. With a clear focus on IAS, the workshop aimed at addressing the following issues: a) CS and policy, b) citizen engagement, and c) CS data management. Nine short presentations provided input on CS and IAS issues. Participants discussed specific topics in several round tables (“world café” style) and reported back their conclusions to the audience and full assembly moderated discussions. Overall, the workshop enabled the sharing of ideas, approaches and best practices regarding CS and IAS. Specific opportunities and pitfalls of using CS data in the whole policy cycle for IAS were recognized. Concerning the implementation of the IAS Regulation, CS data could complement official surveillance systems, and contribute to the early warning of the IAS of Union concern after appropriate validation by the Member States’ competent authorities. CS projects can additionally increase awareness and empower citizens. Attendees pointed out the importance for further public engagement in CS projects on IAS that demonstrate specific initiatives and approaches and analyze lessons learned from past experiences. In addition, the workshop noted that the data gathered from different CS projects on IAS are fragmented. It highlighted the need for using an open and accessible platform to upload data originating from CS sources or to mirror validated data into a single, easy-to-use web service, in line with the EU Open Science Strategic Priority. The workshop provided ten key recommendations of best practices for CS projects on IAS, addressed to researchers, policy makers and implementing authorities, indicating future research and policy directions and opportunities.

Citizen science initiatives have been increasingly used by researchers as a source of occurrence data to model the distribution of alien species. Since citizen science presence-only data suffer from some fundamental issues, efforts have been made to combine these data with those provided by scientifically structured surveys. Surprisingly, only a few studies proposing data integration evaluated the contribution of this process to the effective sampling of species' environmental niches and, consequently, its effect on model predictions on new time intervals. We relied on niche overlap analyses, machine learning classification algorithms and ecological niche models to compare the ability of data from citizen science and scientific surveys, along with their integration, in capturing the realized niche of 13 invasive alien species in Italy. Moreover, we assessed differences in current and future invasion risk predicted by each data set under multiple global change scenarios. We showed that data from citizen science and scientific surveys captured similar species niches though highlighting exclusive portions associated with clearly identifiable environmental conditions. In terrestrial species, citizen science data granted the highest gain in environmental space to the pooled niches, determining an increased future biological invasion risk. A few aquatic species modelled at the regional scale reported a net loss in the pooled niches compared to their scientific survey niches, suggesting that citizen science data may also lead to contraction in pooled niches. For these species, models predicted a lower future biological invasion risk. These findings indicate that citizen science data may represent a valuable contribution to predicting future spread of invasive alien species, especially within national-scale programmes. At the same time, citizen science data collected on species poorly known to citizen scientists, or in strictly local contexts, may strongly affect the niche quantification of these taxa and the prediction of their future biological invasion risk.

Citizen science data can fundamentally advance the natural sciences, but concerns remain about its accuracy, reliability, and overall value. While some studies have evaluated accuracy of citizen science data, few have also assessed its potential contribution to conservation policy. This study focuses on rainfall data collection, with four goals: (1) to examine motivations of, and barriers for, volunteer participation in citizen science; (2) to evaluate accuracy of citizen science rainfall data in comparison to automatic rain gauge data; (3) to incorporate citizen science rainfall datasets into hydrological models; and (4) to apply the hydrologic model to gauge the contribution of citizen science data to the efficient design of payment for hydrological services (PHS) programs. Twelve citizen science volunteers were trained and collected rainfall data between June 2017 and February 2019 across two watersheds in Veracruz, Mexico. We found that these volunteers were highly motivated by conservation values and learning, while only a few volunteers faced barriers related to time availability for making daily measurements. The mean error in daily rainfall, computed by comparing the manual and automated gauge measurements, was less than 1 mm, or 12% of the average daily rainfall. Approximately one-third (29%) and two-thirds (71%) of the errors were attributed to missing data and misread data, respectively. Spatial patterns of rainfall distribution across the watersheds were similar between citizen science and automatic gauge data, revealing a large fraction of rainfall in middle elevations. Furthermore, the results show that if PHS areas are determined using the existing national rainfall network alone, without citizen science data, critical areas that contribute to dry-season flows would be missed. To our knowledge, this is the first citizen science network for collecting rainfall data in Mexico that has produced results that are relevant to conservation policy design.

Citizen science has contributed to biodiversity research and monitoring for hundreds of years. Still, the recent increase in scale, scope, diversity and number of citizen science projects highlights the challenge of designing and implementing good practices around data collection and data curation. The Committee on Data for Science and Technology of the International Council for Science (ICSU-CODATA) and the World Data System (WDS) recently founded a joint Task Group to understand and support good practices for citizen science data validation, data cleaning and curation, and, short- and long- term data management. Research projects conducted by the ICSU-CODATA-WDS Task Group include the development of an initial typology of citizen science data generating tasks, and an exploratory landscape analysis of the state of the data in citizen science. The landscape analysis found that citizen science projects use a wide range of strategies for data validation at numerous stages of the scientific research process. In comparison, practices for data documentation, curation, and long-term management are less advanced. This may limit data discovery and re-use. This work compliments the planned and ongoing efforts of the TDWG Citizen Science Interest Group to advance biodiversity informatics for citizen science. Presenting research on the state of the data in citizen science can promote cross-pollination between the ICSU-CODATA-WDS Task Group and the biodiversity community, and encourage researchers and practitioners to work together to advance citizen science data quality, standards, and interoperability.

Simple SummaryThis work aims to validate a citizen science protocol for monitoring the flight activity of stingless bees. The count of flight activity (entrance, exit, and entrance carrying pollen) filmed in 30 s videos was compared among three different groups: “original” citizen scientists (group that filmed and performed the count in their own videos), “replicator” citizen scientists (group of citizen scientists who performed flight activity counts on videos shot by other citizen scientists), and experts (researchers who work with bees and who performed the counts on videos shot by citizen scientists). The analysis was divided into two levels: perception (detection of activity in videos) and counting. The results of this analysis revealed that citizen scientists and experts have similar perception and count of bee entrance and exit activity, as no statistical differences were found in these two items. However, replicator citizen scientists noticed more bees carrying pollen than original citizen scientists and experts. Despite this, considering only the videos in which the groups agreed on the presence of pollen, the count was similar for both. These results enabled the validation of the protocol and indicated high quality of data produced by individuals who participate in scientific practices following a citizen science approach.Although the quality of citizen science (CS) data is often a concern, evidence for high-quality CS data increases in the scientific literature. This study aimed to assess the data reliability of a structured CS protocol for monitoring stingless bees’ flight activity. We tested (1) data accuracy for replication among volunteers and for expert validation and (2) precision, comparing dispersion between citizen scientists and expert data. Two distinct activity dimensions were considered: (a) perception of flight activity and (b) flight activity counts (entrances, exits, and pollen load). No significant differences were found among groups regarding entrances and exits. However, replicator citizen scientists presented a higher chance of perceiving pollen than original data collectors and experts, likely a false positive. For those videos in which there was an agreement about pollen presence, the effective pollen counts were similar (with higher dispersion for citizen scientists), indicating the reliability of CS-collected data. The quality of the videos, a potential source of variance, did not influence the results. Increasing practical training could be an alternative to improve pollen data quality. Our study shows that CS provides reliable data for monitoring bee activity and highlights the relevance of a multi-dimensional approach for assessing CS data quality.

Understanding global patterns of functional diversity is essential for exploring ecosystem functioning, yet our current knowledge is limited to specific regions and geographically restricted datasets.. Meanwhile, rapidly growing citizen science initiatives, such as iNaturalist or Pl@ntNet, have generated millions of ground-level species observations across the globe. Despite citizen science species observations being noisy and opportunistically sampled, previous studies have shown that integrating them with large functional trait databases enables the creation of global trait maps with promising accuracy. However, aggregating citizen science data only allows for the generation of relatively sparse and coarse trait maps, e.g. at 0.2 to 2.0 degree spatial resolution.Here, by using such citizen science data in concert with vegetation surveys and high-resolution Earth observation data, we extend this approach to model the relationships between functional traits and their structural and environmental determinants, providing global trait maps with globally continuous coverage and high spatial resolution (up to 1km). This fusion of ground-based citizen science and continuous satellite data allows us not only to map more than 30 ecologically relevant traits but also to derive crucial functional diversity metrics at a global scale. These metrics—such as functional richness and evenness—provide new opportunities to explore the role of functional diversity in ecosystem processes, particularly in areas previously lacking in data availability.Our approach presents a scalable framework to advance understanding of plant functional traits and diversity, opening the door to new insights on how ecosystems may respond to an increasingly variable and extreme climate.

Chinese tallow is a non-native invasive tree expanding in range and abundance throughout the southern United States. Several biogeographical studies mapping tallow distribution and examining key underlying environmental factors relied on the U.S. Forest Service Forest Inventory and Analysis (FIA) data, representing forestlands at scales of ~2400 ha. However, given that most invasive trees, like tallow, are cosmopolitan and dynamic in nature, FIA data fails to capture the extent and severity of the invasion especially outside areas classified as forestlands. To develop tallow maps that more adequately depict its distribution at finer spatial scales and to capture observations in non-forestlands, we combined verified citizen science observations with FIA data. Further, we described spatiotemporal patterns and compared citizen science to FIA and other previously published distribution maps. From our work, although tallow is prevalent in the south, Louisiana, Texas, and Mississippi were the most invaded states. Tallow was associated with flatwoods and prairie grasslands of the Gulf Coast. Annual extreme minimum temperatures of less than −12.2 °C (10 °F) represented the northern limit of naturalized tallow populations. Tallow’s northward and inland expansion was captured in citizen science and FIA data, indicating a tallow spread rate ranging from 5 to 20 km annually over the last decade. Systematic sampling, such as FIA, and citizen science data both have their own unique pitfalls. However, the use of citizen science data can complement invasive plant distribution mapping, especially when combined with data from established systematic monitoring networks. This approach provides for a more complete understanding of invasive tree extent and spatiotemporal dynamics across large landscapes.

The research and conservation community has successfully harnessed the wealth of ecological knowledge found in unprecedented volumes of citizen science (CS) data world‐wide. However, few examples exist of the use of CS data to directly inform policy. Current examples of applications of CS data mainly stem from programs that are restricted in scope (e.g. defined protocols, restricted sampling time frame), and the potential use of unrestricted CS data to inform policy remains largely untapped. Here, we make a call for moving beyond questioning the reliability of CS data and present a case study of how the US Fish and Wildlife Service (USFWS) used information from an unrestricted CS program (eBird) to inform levels of exposure to collision risk for wind energy development. Policy implications. The USFWS made the technical recommendation to use eBird abundance estimates for the bald eagle as the only source of information to define low‐risk collision areas as part of the agency's wind energy permitting process. Our study contributes a clear pathway of how to realize the potential of unrestricted CS programs for generating the evidence base needed to inform policy decisions.

Background. Volunteers’ participation in scientific research has increased in recent decades. Citizen science (CS) data have been used in quantitative ecology to analyse species ranges by means of species distribution models. We investigated the Italian distribution of five large saproxylic beetles (big five), to describe their niche space, paramount areas for their conservation, and conservation gaps. Methods. CS data from two projects, climate and environmental variables were used to produce Habitat suitability (HS) maps for each species and averaged HS maps. The big five’s conservation status was assessed interpolating HS maps with the distribution of protected areas, concomitantly identifying conservation gaps. Results. The pre-alpine and Apennines arcs, north-eastern Sicily and eastern Sardinia, were identified as conservation’s hotspots. Ranking HS levels from minimum to optimal, the extent of conservation gaps decreases as environmental suitability for the big five increases. Conclusions. For the first time in Italy, CS data have been used to investigate niche space of the largest protected saproxylic beetles and analyse the distribution of their suitable habitat. The resulting HS raster maps and vector layers, reporting HS value in all Italian protected areas (n° 3771), were provided and discussed, reporting an application example for conservation purposes.

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.

BackgroundCitizen science is becoming a mainstream approach of baseline data collection to monitor biodiversity and climate change. Dragonflies (Odonata) have been ranked as the highest priority group in biodiversity monitoring for global warming. Ischnura senegalensis Rambur has been designated a biological indicator of climate change and is being monitored by the citizen science project “Korean Biodiversity Observation Network.” This study has been performed to understand changes in the distribution range of I. senegalensis in response to climate change using citizen science data in South Korea.ResultsWe constructed a dataset of 397 distribution records for I. senegalensis, ranging from 1980 to 2020. The number of records sharply increased over time and space, and in particular, citizen science monitoring data accounted for the greatest proportion (58.7%) and covered the widest geographical range. This species was only distributed in the southern provinces until 2010 but was recorded in the higher latitudes such as Gangwon-do, Incheon, Seoul, and Gyeonggi-do (max. Paju-si, 37.70° latitude) by 2020. A species distribution model showed that the annual mean temperature (Bio1; 63.2%) and the maximum temperature of the warmest month (Bio5; 16.7%) were the most critical factors influencing its distribution. Future climate change scenarios have predicted an increase in suitable habitats for this species.ConclusionsThis study is the first to show the northward expansion in the distribution range of I. senegalensis in response to climate warming in South Korea over the past 40 years. In particular, citizen science was crucial in supplying critical baseline data to detect the distribution change toward higher latitudes. Our results provide new insights on the value of citizen science as a tool for detecting the impact of climate change on ecosystems in South Korea.

<p>INTAROS is a Horizon 2020 research and innovation project developing an integrated Arctic Observation System by extending, improving, and unifying existing systems in the different regions of the Arctic. INTAROS integrates distributed repositories hosting data from ocean, atmosphere, cryosphere and land, including scientific, community-based monitoring (CBM) and citizen science (CS) data. Throughout the project, INTAROS has been working closely with several local communities and citizen science programs across the Arctic, to develop strategies and methods for ingestion of data into repositories enabling the communities to maintain and share data. A number of these CBM and CS data collections have been registered in the INTAROS Data Catalogue. Some of these collections are hosted and sustained by large international programs such as PISUNA, eBird, Secchi Disk Study and GLOBE Observer. Registration in the INTAROS Data Catalogue contributes to making these important data collections better known in a wider community of users with a vested interest in the Arctic. It also enables sharing of metadata through open standards for inclusion in other Arctic data systems. This catalogue is a key component in INTAROS, enabling users to search for data across the targeted spheres to assess their usefulness in applications and geographic areas. The catalogue is based on a world-leading system for data management, the Comprehensive Knowledge Archive Network (CKAN). With rich functionality offered out of the box combined with a flexible extension mechanism, CKAN allows for quickly setting up a fully functional data catalogue. The CKAN open-source community offers numerous extensions that can be used as-is or adapted to implement customised functionality for specific user communities. To hold additional metadata elements requested by the partners we modified the standard database schema of CKAN. The presentation will focus on the current capabilities and plans for sustaining and enhancing the INTAROS Data Catalogue.</p>

Testing a citizen science water monitoring approach in Tunisia