The Value of Long-Term Stream Invertebrate Data Collected by Citizen Scientists.
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ABSTRACT:
SUBMITTER: Edwards PM
PROVIDER: S-EPMC4847766 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:[This corrects the article DOI: 10.1371/journal.pone.0153713.].
| S-EPMC4877082 | biostudies-literature
Project description:The active collection of wildlife sighting data by trained observers is expensive, restricted to small geographical areas and conducted infrequently. Reporting of wildlife sightings by members of the public provides an opportunity to collect wildlife data continuously over wider geographical areas, at lower cost. We used individual koala sightings reported by members of the public between 1997 and 2013 in South-East Queensland, Australia (n = 14,076 koala sightings) to describe spatial and temporal trends in koala presence, to estimate koala sighting density and to identify biases associated with sightings. Temporal trends in sightings mirrored the breeding season of koalas. Sightings were high in residential areas (63%), followed by agricultural (15%), and parkland (12%). The study area was divided into 57,780 one-square kilometer grid cells and grid cells with no sightings of koalas decreased over time (from 35% to 21%) indicative of a greater level of spatial overlap of koala home ranges and human activity areas over time. The density of reported koala sightings decreased as distance from primary and secondary roads increased, indicative of a higher search effort near roads. Our results show that koala sighting data can be used to refine koala distribution and population estimates derived from active surveying, on the condition that appropriate bias correction techniques are applied. Collecting koala absence and search effort information and conducting repeated searches for koalas in the same areas are useful approaches to improve the quality of sighting data in citizen science programs.
| S-EPMC6624211 | biostudies-literature
Project description:We compiled a lake-water clarity database using publically available, citizen volunteer observations made between 1938 and 2012 across eight states in the Upper Midwest, USA. Our objectives were to determine (1) whether temporal trends in lake-water clarity existed across this large geographic area and (2) whether trends were related to the lake-specific characteristics of latitude, lake size, or time period the lake was monitored. Our database consisted of >140,000 individual Secchi observations from 3,251 lakes that we summarized per lake-year, resulting in 21,020 summer averages. Using Bayesian hierarchical modeling, we found approximately a 1% per year increase in water clarity (quantified as Secchi depth) for the entire population of lakes. On an individual lake basis, 7% of lakes showed increased water clarity and 4% showed decreased clarity. Trend direction and strength were related to latitude and median sample date. Lakes in the southern part of our study-region had lower average annual summer water clarity, more negative long-term trends, and greater inter-annual variability in water clarity compared to northern lakes. Increasing trends were strongest for lakes with median sample dates earlier in the period of record (1938-2012). Our ability to identify specific mechanisms for these trends is currently hampered by the lack of a large, multi-thematic database of variables that drive water clarity (e.g., climate, land use/cover). Our results demonstrate, however, that citizen science can provide the critical monitoring data needed to address environmental questions at large spatial and long temporal scales. Collaborations among citizens, research scientists, and government agencies may be important for developing the data sources and analytical tools necessary to move toward an understanding of the factors influencing macro-scale patterns such as those shown here for lake water clarity.
| S-EPMC4005745 | biostudies-literature
Project description:This dataset represents expert-validated occurrence records of calling frogs across Australia collected via the national citizen science project FrogID (http://www.frogid.net.au). FrogID relies on participants recording calling frogs using smartphone technology, after which point the frogs are identified by expert validators, resulting in a database of georeferenced frog species records. This dataset represents one full year of the project (10 November 2017-9 November 2018), including 54,864 records of 172 species, 71% of the known frog species in Australia. This is the first instalment of the dataset, and we anticipate providing updated datasets on an annual basis.
| S-EPMC7040047 | 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:BackgroundLong-term health consequences following acute SARS-CoV-2 infection, referred to as post-COVID-19 condition or Long COVID, are increasing, with population-based prevalence estimates for adults at around 20%. Persons affected by Long COVID report various health problems, yet evidence to guide clinical decision making remains scarce.ObjectiveThe present study aimed to identify Long COVID research priorities using a citizen science approach and solely considering the needs of those affected.MethodsThis citizen science study followed an iterative process of patient needs identification, evaluation and prioritisation. A Long COVID Citizen Science Board (21 persons with Long COVID, and seven with myalgic encephalomyelitis/chronic fatigue syndrome) and a Long COVID Working Group (25 persons with Long COVID, four patients with myalgic encephalomyelitis/chronic fatigue syndrome and one relative) were formed. The study included four activities: three remote meetings and one online survey. First, Board members identified the needs and research questions. Second, Working Group members and persons affected by Long COVID (241 respondents, 85.5% with Long COVID, 14.5% with myalgic encephalomyelitis/chronic fatigue syndrome and 7.1% relatives) evaluated the research questions on a 1-5 Likert scale using an online survey. Then the Board gave feedback on this evaluation. Finally, Board members set the priorities for research through voting and discussion.ResultsSixty-eight research questions were generated by the Board and categorised into four research domains (medicine, healthcare services, socioeconomics and burden of disease) and 14 subcategories. Their average importance ratings were moderate to high and varied from 3.41 (standard deviation = 1.16) for sex-specific diagnostics to 4.86 (standard deviation = 0.41) for medical questions on treatment. Five topics were prioritised: "treatment, rehabilitation and chronic care management", "availability of interfaces for treatment continuity", "availability of healthcare structures", "awareness and knowledge among professionals" and "prevalence of Long COVID in children and adolescents".ConclusionsTo our knowledge, this is the first study developing a citizen-driven, explicitly patient-centred research agenda with persons affected by Long COVID, setting it apart from existing multi-stakeholder efforts. The identified priorities could guide future research and funding allocation. Our methodology establishes a framework for citizen-driven research agendas, suitable for transfer to other diseases.
| S-EPMC9046008 | biostudies-literature
Project description:Online citizen science projects such as GalaxyZoo1, Eyewire2 and Phylo3 have proven very successful for data collection, annotation and processing, but for the most part have harnessed human pattern-recognition skills rather than human creativity. An exception is the game EteRNA4, in which game players learn to build new RNA structures by exploring the discrete two-dimensional space of Watson-Crick base pairing possibilities. Building new proteins, however, is a more challenging task to present in a game, as both the representation and evaluation of a protein structure are intrinsically three-dimensional. We posed the challenge of de novo protein design in the online protein-folding game Foldit5. Players were presented with a fully extended peptide chain and challenged to craft a folded protein structure and an amino acid sequence encoding that structure. After many iterations of player design, analysis of the top-scoring solutions and subsequent game improvement, Foldit players can now-starting from an extended polypeptide chain-generate a diversity of protein structures and sequences that encode them in silico. One hundred forty-six Foldit player designs with sequences unrelated to naturally occurring proteins were encoded in synthetic genes; 56 were found to be expressed and soluble in Escherichia coli, and to adopt stable monomeric folded structures in solution. The diversity of these structures is unprecedented in de novo protein design, representing 20 different folds-including a new fold not observed in natural proteins. High-resolution structures were determined for four of the designs, and are nearly identical to the player models. This work makes explicit the considerable implicit knowledge that contributes to success in de novo protein design, and shows that citizen scientists can discover creative new solutions to outstanding scientific challenges such as the protein design problem.
| S-EPMC6701466 | biostudies-literature
Project description:We are currently in the midst of Earth's sixth extinction event, and measuring biodiversity trends in space and time is essential for prioritizing limited resources for conservation. At the same time, the scope of the necessary biodiversity monitoring is overwhelming funding for professional scientific monitoring. In response, scientists are increasingly using citizen science data to monitor biodiversity. But citizen science data are 'noisy', with redundancies and gaps arising from unstructured human behaviours in space and time. We ask whether the information content of these data can be maximized for the express purpose of trend estimation. We develop and execute a novel framework which assigns every citizen science sampling event a marginal value, derived from the importance of an observation to our understanding of overall population trends. We then make this framework predictive, estimating the expected marginal value of future biodiversity observations. We find that past observations are useful in forecasting where high-value observations will occur in the future. Interestingly, we find high value in both 'hotspots', which are frequently sampled locations, and 'coldspots', which are areas far from recent sampling, suggesting that an optimal sampling regime balances 'hotspot' sampling with a spread across the landscape.
| S-EPMC6790778 | biostudies-literature
Project description:The koala (Phascolarctos cinereus) occurs in the eucalypt forests of eastern and southern Australia and is currently threatened by habitat fragmentation, climate change, sexually transmitted diseases, and low genetic variability throughout most of its range. Using data collected during the Great Koala Count (a 1-day citizen science project in the state of South Australia), we developed generalized linear mixed-effects models to predict habitat suitability across South Australia accounting for potential errors associated with the dataset. We derived spatial environmental predictors for vegetation (based on dominant species of Eucalyptus or other vegetation), topographic water features, rain, elevation, and temperature range. We also included predictors accounting for human disturbance based on transport infrastructure (sealed and unsealed roads). We generated random pseudo-absences to account for the high prevalence bias typical of citizen-collected data. We accounted for biased sampling effort along sealed and unsealed roads by including an offset for distance to transport infrastructures. The model with the highest statistical support (wAIC c ? 1) included all variables except rain, which was highly correlated with elevation. The same model also explained the highest deviance (61.6%), resulted in high R (2)(m) (76.4) and R (2)(c) (81.0), and had a good performance according to Cohen's ? (0.46). Cross-validation error was low (? 0.1). Temperature range, elevation, and rain were the best predictors of koala occurrence. Our models predict high habitat suitability in Kangaroo Island, along the Mount Lofty Ranges, and at the tips of the Eyre, Yorke and Fleurieu Peninsulas. In the highest-density region (5576 km(2)) of the Adelaide-Mount Lofty Ranges, a density-suitability relationship predicts a population of 113,704 (95% confidence interval: 27,685-199,723; average density = 5.0-35.8 km(-2)). We demonstrate the power of citizen science data for predicting species' distributions provided that the statistical approaches applied account for some uncertainties and potential biases. A future improvement to citizen science surveys to provide better data on search effort is that smartphone apps could be activated at the start of the search. The results of our models provide preliminary ranges of habitat suitability and population size for a species for which previous data have been difficult or impossible to gather otherwise.
| S-EPMC4201425 | biostudies-literature
Project description:Coral reefs are threatened by numerous global and local stressors. In the face of predicted large-scale coral degradation over the coming decades, the importance of long-term monitoring of stress-induced ecosystem changes has been widely recognised. In areas where sustained funding is unavailable, citizen science monitoring has the potential to be a powerful alternative to conventional monitoring programmes. In this study we used data collected by volunteers in Southeast Sulawesi (Indonesia), to demonstrate the potential of marine citizen science programmes to provide scientifically sound information necessary for detecting ecosystem changes in areas where no alternative data are available. Data were collected annually between 2002 and 2012 and consisted of percent benthic biotic and abiotic cover and fish counts. Analyses revealed long-term coral reef ecosystem change. We observed a continuous decline of hard coral, which in turn had a significant effect on the associated fishes, at community, family and species levels. We provide evidence of the importance of marine citizen science programmes in detecting long-term ecosystem change as an effective way of delivering conservation data to local government and national agencies. This is particularly true for areas where funding for monitoring is unavailable, resulting in an absence of ecological data. For citizen science data to contribute to ecological monitoring and local decision-making, the data collection protocols need to adhere to sound scientific standards, and protocols for data evaluation need to be available to local stakeholders. Here, we describe the monitoring design, data treatment and statistical analyses to be used as potential guidelines in future marine citizen science projects.
| S-EPMC6326458 | biostudies-literature