Accessible data curation and analytics for international-scale citizen science datasets.
Ontology highlight
ABSTRACT:
SUBMITTER: Murray B
PROVIDER: S-EPMC8608807 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Integrative modeling methods can now enable macrosystem-level understandings of biodiversity patterns, such as range changes resulting from shifts in climate or land use, by aggregating species-level data across multiple monitoring sources. This requires ensuring that taxon interpretations match up across different sources. While encouraging checklist standardization is certainly an option, coercing programs to change species lists they have used consistently for decades is rarely successful. Here we demonstrate a novel approach for tracking equivalent names and concepts, applied to a network of 10 regional programs that use the same protocols (so-called "Pollard walks") to monitor butterflies across America north of Mexico. Our system involves, for each monitoring program, associating the taxonomic authority (in this case one of three North American butterfly fauna treatments: Pelham, 2014; North American Butterfly Association, Inc., 2016; Opler & Warren, 2003) that shares the most similar overall taxonomic interpretation to the program's working species list. This allows us to define each term on each program's list in the context of the appropriate authority's species concept and curate the term alongside its authoritative concept. We then aligned the names representing equivalent taxonomic concepts among the three authorities. These stepping stones allow us to bridge a species concept from one program's species list to the name of the equivalent in any other program, through the intermediary scaffolding of aligned authoritative taxon concepts. Using a software tool we developed to access our curation system, a user can link equivalent species concepts between data collecting agencies with no specialized knowledge of taxonomic complexities.
| S-EPMC7395600 | biostudies-literature
Project description:Large-scale observational data from citizen science efforts are becoming increasingly common in ecology, and researchers often choose between these and data from intensive local-scale studies for their analyses. This choice has potential trade-offs related to spatial scale, observer variance, and interannual variability. Here we explored this issue with phenology by comparing models built using data from the large-scale, citizen science USA National Phenology Network (USA-NPN) effort with models built using data from more intensive studies at Long Term Ecological Research (LTER) sites. We built statistical and process based phenology models for species common to each data set. From these models, we compared parameter estimates, estimates of phenological events, and out-of-sample errors between models derived from both USA-NPN and LTER data. We found that model parameter estimates for the same species were most similar between the two data sets when using simple models, but parameter estimates varied widely as model complexity increased. Despite this, estimates for the date of phenological events and out-of-sample errors were similar, regardless of the model chosen. Predictions for USA-NPN data had the lowest error when using models built from the USA-NPN data, while LTER predictions were best made using LTER-derived models, confirming that models perform best when applied at the same scale they were built. This difference in the cross-scale model comparison is likely due to variation in phenological requirements within species. Models using the USA-NPN data set can integrate parameters over a large spatial scale while those using an LTER data set can only estimate parameters for a single location. Accordingly, the choice of data set depends on the research question. Inferences about species-specific phenological requirements are best made with LTER data, and if USA-NPN or similar data are all that is available, then analyses should be limited to simple models. Large-scale predictive modeling is best done with the larger-scale USA-NPN data, which has high spatial representation and a large regional species pool. LTER data sets, on the other hand, have high site fidelity and thus characterize inter-annual variability extremely well. Future research aimed at forecasting phenology events for particular species over larger scales should develop models that integrate the strengths of both data sets.
| S-EPMC7378950 | biostudies-literature
Project description:Many animals rely on vocal communication for mating advertisement, territorial displays, and warning calls. Advertisement calls are species-specific, serve as a premating isolation mechanism, and reinforce species boundaries. Nevertheless, there is a great deal of interspecific variability of advertisement calls. Quantifying the variability of calls among individuals within a species and across species is critical to understand call evolution and species boundaries, and may build a foundation for further research in animal communication. However, collecting a large volume of advertisement call recordings across a large geographic area has traditionally posed a logistical barrier. We used data from the continental-scale citizen science project FrogID to investigate the spatial and temporal patterns of call characteristics in six Australian frog species. We found intraspecific call variability in both call duration and peak frequency across species. Using resampling methods, we show that variability in call duration and peak frequency was related to the number of individuals recorded, the geographic area encompassed by those individuals, and the intra-annual time difference between those recordings. We conclude that in order to accurately understand frog advertisement call variation, or "anuran accents," the number of individuals in a sample must be numerous (N ≥ 20), encompass a large geographic area relative to a species' range, and be collected throughout a species' calling season.
| S-EPMC7663080 | biostudies-literature
Project description:Trait-based ecology is greatly informed by large datasets for the analyses of inter- and intraspecific trait variation (ITV) in plants. This is especially true in trait-based agricultural research where crop ITV is high, yet crop trait data remains limited. Based on farmer-led collections, we developed and evaluated the first citizen science plant trait initiative. Here we generated a dataset of eight leaf traits for a commercially important crop species (Daucus carota), sampled from two distinct regions in Canada, which is 25-fold larger than datasets available in existing trait databases. Citizen-collected trait data supported analyses addressing theoretical and applied questions related to (i) intraspecific trait dimensionality, (ii) the extent and drivers of ITV, and (iii) the sampling intensity needed to derive accurate trait values. Citizen science is a viable means to enhance functional trait data coverage across terrestrial ecosystems, and in doing so, can directly support theoretical and applied trait-based analyses of plants.
| S-EPMC6823441 | biostudies-literature
Project description:Abstract The GLOBE Program's GLOBE Observer application is a free citizen science mobile data collection and visualization tool compatible with iOS and Android operating systems. Citizen scientists armed with the app can report the mosquito larval habitats they identify using the GLOBE Mosquito Habitat Mapper tool. This data can complement the climate, weather, and land cover data obtained from satellite measurements by scientists who develop risk models for mosquito‐borne diseases. Public participation in mosquito surveillance research provides the opportunity to obtain the volume, velocity and variety of data needed to fight the threat of vector‐borne diseases, especially in under‐resourced communities with minimal to no municipal surveillance and mitigation services. GLOBE Mosquito Habitat Mappers document and describe potential and active mosquito larval habitats in and around their homes and communities. An easy‐to‐use pictorial interface enables users to geolocate and describe oviposition sites encountered, count and identify mosquito larvae, and when appropriate, eliminate the larval habitats. During Mosquito Habitat Mapper's first 3 years of use, over 24,000 data observations have been reported throughout the world. This technical report summarizes GLOBE Mosquito Habitat Mapper data reported by GLOBE citizen scientists from three regions: Africa, Asia and the Pacific Islands, and Latin America and the Caribbean. Localized mosquito larvae distribution patterns were examined by comparing data collected in three cities in Senegal–Dakar, Touba, and Thilmakha. The Senegal data show habitat and genera differences among mosquitoes identified by citizen scientists in the cities and illustrates the potential of the app for community‐based surveillance and research. Key Points GLOBE Observer is The GLOBE Program's mobile app. The GLOBE Mosquito Habitat Mapper on this platform enables citizen scientists to identify, describe and report mosquito larvae and habitats GLOBE Mosquito Habitat Mappers not only collect data, but participate in source reduction and reduce risk of disease in their community GLOBE Mosquito Habitat Mapper data has practical applications in municipal surveillance as well as in mosquito vector borne disease research
| S-EPMC8527845 | biostudies-literature
Project description:Citizen science is mainstream: millions of people contribute data to a growing array of citizen science projects annually, forming massive datasets that will drive research for years to come. Many citizen science projects implement a "leaderboard" framework, ranking the contributions based on number of records or species, encouraging further participation. But is every data point equally "valuable?" Citizen scientists collect data with distinct spatial and temporal biases, leading to unfortunate gaps and redundancies, which create statistical and informational problems for downstream analyses. Up to this point, the haphazard structure of the data has been seen as an unfortunate but unchangeable aspect of citizen science data. However, we argue here that this issue can actually be addressed: we provide a very simple, tractable framework that could be adapted by broadscale citizen science projects to allow citizen scientists to optimize the marginal value of their efforts, increasing the overall collective knowledge.
| S-EPMC6619805 | biostudies-literature
Project description:Appropriate large-scale citizen-science data present important new opportunities for biodiversity modelling, due in part to the wide spatial coverage of information. Recently proposed occupancy modelling approaches naturally incorporate random effects in order to account for annual variation in the composition of sites surveyed. In turn this leads to Bayesian analysis and model fitting, which are typically extremely time consuming. Motivated by presence-only records of occurrence from the UK Butterflies for the New Millennium data base, we present an alternative approach, in which site variation is described in a standard way through logistic regression on relevant environmental covariates. This allows efficient occupancy model-fitting using classical inference, which is easily achieved using standard computers. This is especially important when models need to be fitted each year, typically for many different species, as with British butterflies for example. Using both real and simulated data we demonstrate that the two approaches, with and without random effects, can result in similar conclusions regarding trends. There are many advantages to classical model-fitting, including the ability to compare a range of alternative models, identify appropriate covariates and assess model fit, using standard tools of maximum likelihood. In addition, modelling in terms of covariates provides opportunities for understanding the ecological processes that are in operation. We show that there is even greater potential; the classical approach allows us to construct regional indices simply, which indicate how changes in occupancy typically vary over a species' range. In addition we are also able to construct dynamic occupancy maps, which provide a novel, modern tool for examining temporal changes in species distribution. These new developments may be applied to a wide range of taxa, and are valuable at a time of climate change. They also have the potential to motivate citizen scientists.
| S-EPMC5362231 | biostudies-literature
Project description:As libraries struggle to keep pace with the changing societal landscape, emerging practices such as citizen science (CS) initiatives are being incorporated to reinforce the idea of public libraries as gathering, meeting, and collaboration spaces within the context of shared community and shared learning resources. However, there is little empirical evidence of whether the most open and participatory ways that CS puts forward can converge with and be nurtured by the essence of public libraries. Also, the roles of librarians and users in the 'next generation public library' have been under-developed. As the number of CS initiatives at public libraries grows, so does the need to collect evidence on the impact and the capacity of assimilation of CS practices. The data describes librarians and users' perceptions of participating in a citizen science project. Two hands-on activities for librarians of the Barcelona Network of Public Libraries were implemented. One was a training course for 30 librarians from 24 libraries which allowed them to envisage citizen science implementation in each library. The second activity consisted in the co-creation of a citizen social science project. 40 library users, 7 librarians from 3 different cities, and professional scientists, were involved. The data on librarians and users' perception was collected through participant observation, surveys, and a focus group to identify strengths and challenges of implementing citizen science at public libraries. The data covers librarians and users attitudes towards citizen science, their motivations to participate, their perceived ability to implement a citizen science project (as for librarians) or to contribute to science (as for library users), and the participants intention to keep engaged with citizen science, drawing on the Theory of Planned Behavior. Responses to closed-ended survey questions are analyzed at a descriptive level. The qualitative feedback from the focus group and the open-ended survey question on motivations is subjected to a thematic analysis. The data offers interesting insights to identify opportunities and challenges of implementing citizen science at public libraries, contributing to the debate over the public library's mission as local community hub.
| S-EPMC8717417 | biostudies-literature
Project description:Web-based citizen science often involves the classification of image features by large numbers of minimally trained volunteers, such as the identification of lunar impact craters under the Moon Zoo project. Whilst such approaches facilitate the analysis of large image data sets, the inexperience of users and ambiguity in image content can lead to contamination from false positive identifications. We give an approach, using Linear Poisson Models and image template matching, that can quantify levels of false positive contamination in citizen science Moon Zoo crater annotations. Linear Poisson Models are a form of machine learning which supports predictive error modelling and goodness-of-fits, unlike most alternative machine learning methods. The proposed supervised learning system can reduce the variability in crater counts whilst providing predictive error assessments of estimated quantities of remaining true verses false annotations. In an area of research influenced by human subjectivity, the proposed method provides a level of objectivity through the utilisation of image evidence, guided by candidate crater identifications.
| S-EPMC7114961 | biostudies-literature
Project description:Citizen science platforms are increasingly growing, and, storing a huge amount of data on species locations, they provide researchers with essential information to develop sound strategies for species conservation. However, the lack of information on surveyed sites (i.e., where the observers did not record the target species) and sampling effort (e.g., the number of surveys at a given site, by how many observers, and for how much time) strongly limit the use of citizen science data. Thus, we examined the advantage of using an observer-oriented approach (i.e., considering occurrences of species other than the target species collected by the observers of the target species as pseudo-absences and additional predictors relative to the total number of observations, observers, and days in which locations were collected in a given sampling unit, as proxies of sampling effort) to develop species distribution models. Specifically, we considered 15 mammal species occurring in Italy and compared the predictive accuracy of the ensemble predictions of nine species distribution models carried out considering random pseudo-absences versus observer-oriented approach. Through cross-validations, we found that the observer-oriented approach improved species distribution models, providing a higher predictive accuracy than random pseudo-absences. Our results showed that species distribution modeling developed using pseudo-absences derived citizen science data outperform those carried out using random pseudo-absences and thus improve the capacity of species distribution models to accurately predict the geographic range of species when deriving robust surrogate of sampling effort.
| S-EPMC7663073 | biostudies-literature