scholarly journals Estimating disease vector population size from citizen science data

2021 ◽  
Vol 18 (184) ◽  
Author(s):  
Tam Tran ◽  
W. Tanner Porter ◽  
Daniel J. Salkeld ◽  
Melissa A. Prusinski ◽  
Shane T. Jensen ◽  
...  
Keyword(s):  
Data Collection ◽  
Citizen Science ◽  
Large Scale ◽  
Species Abundance ◽  
Natural World ◽  
Tick Population ◽  
Science Data ◽  
Tick Abundance ◽  
Vector Population ◽  
Public Datasets

Citizen science projects have the potential to address hypotheses requiring extremely large datasets that cannot be collected with the financial and labour constraints of most scientific projects. Data collection by the general public could expand the scope of scientific enquiry if these data accurately capture the system under study. However, data collection inconsistencies by the untrained public may result in biased datasets that do not accurately represent the natural world. In this paper, we harness the availability of scientific and public datasets of the Lyme disease tick vector to identify and account for biases in citizen science tick collections. Estimates of tick abundance from the citizen science dataset correspond moderately with estimates from direct surveillance but exhibit consistent biases. These biases can be mitigated by including factors that may impact collector participation or effort in statistical models, which, in turn, result in more accurate estimates of tick population sizes. Accounting for collection biases within large-scale, public participation datasets could update species abundance maps and facilitate using the wealth of citizen science data to answer scientific questions at scales that are not feasible with traditional datasets.

Ten years of citizen science data collection of wetland plants in an urban protected area

2015 ◽  
Vol 162 (4) ◽  
pp. 365-373 ◽  
Author(s):  
Fabrizio Buldrini ◽  
Antinisca Simoncelli ◽  
Stefania Accordi ◽  
Giovanna Pezzi ◽  
Daniele Dallai
Keyword(s):  
Data Collection ◽  
Citizen Science ◽  
Protected Area ◽  
Wetland Plants ◽  
Science Data

Design of Microplastics Citizen Science Kit

2020 ◽  
Author(s):  
Rachael Hughson-Gill
Keyword(s):  
Data Collection ◽  
Citizen Science ◽  
Large Scale ◽  
Positive Impact ◽  
Research Process ◽  
Freshwater Ecosystems ◽  
Freshwater Systems ◽  
The Public ◽  
Plastic Bags ◽  
The Impact

<p>Microplastics are an ever-increasing problem. Every river that was tested in a recent study found the presence of microplastics, with 80% of all plastic in the ocean coming from upstream. Despite this, there is little understanding into the abundance of plastic, its characteristics and the full impact that is it having on marine, freshwater ecosystems and wider ecological systems.</p><p> </p><p>Current fresh water monitoring does not consider the fluid dynamics of rivers, is difficult to use and is inaccessible to the wider public. My project will focus on creating a product that allows for the large-scale data collection of microplastic through citizen science. Allowing groups of people to analyse their local natural environment for the presence and abundance of microplastics within the water. This method of data collection could provide information on a scale that is not possible with traditional methods and would allow for the comparison between freshwater systems. This comparison is fundamental to begin to fill the knowledge gaps around the understanding of microplastics.</p><p> </p><p>Inaccessibility of monitoring to the public is not just through tools but also through the current communication of data with research rarely breaking into the public domain. Citizen science offers not just an improvement in understanding but also offers an opportunity for engagement with the public body. Increasing awareness of the impact of habits round plastic through the sharing of monitoring data can generate the much-needed change on both an individual and policy level to address the problem from the source. This method of change through public opinion can be seen to have an effect on freshwater systems through microbeads ban, plastic bags, plastic straws and industrial pollution regulation.</p><p> </p><p>Through the creation of this product a multidisciplinary approach that blends engineering and design practices is implemented. The wholistic approach to creation is something that is fundamental in the success of tools and therefore the success of the research that is implemented through them. A tool such as this whose function is within the public engagement of its use - increased awareness, as well as the outcome of its use - microplastics data, is required to have an engaging user experience as well as data integrity implemented through engineering design.</p><p> </p><p>This project offers an opportunity to show the importance of the design process within research tools to aid the research process and the positive impact that can come from it.</p>

scholarly journals Entomological citizen science in Canada

2017 ◽  
Vol 149 (6) ◽  
pp. 774-785 ◽  
Author(s):  
John H. Acorn
Keyword(s):  
Citizen Science ◽  
Natural World ◽  
Voluntary Participation ◽  
Scientific Process ◽  
Short Term ◽  
Science Data ◽  
Geographic Ranges ◽  
Science Projects ◽  
Online Databases ◽  
Gathering Data

AbstractCitizen science involves voluntary participation in the scientific process, typically by gathering data in order to monitor some aspect of the natural world. Entomological citizen science, as an extension of traditional amateur entomology, is an active field in Canada, with online databases such as eButterfly and BugGuide attracting both contributors and database users. As well, traditional amateur entomology continues to be important in Canada, as do short-term insect-themed educational events, the involvement of amateurs in entomological societies, and online crowdsourcing initiatives. Success of citizen science projects can be measured in many ways. In terms of published papers that analyse trends in citizen science data, Canadian projects have only begun to deliver. More valuable are particular records that improve our knowledge of geographic ranges and phenology. In terms of the endurance of particular projects, and the willingness of volunteers to participate, citizen science entomology in Canada is clearly a success. However, quality control of citizen science data remains an issue for some projects. As well, challenges remain with respect to balancing the goals of researchers, participants, and supporting institutions.

scholarly journals Winners and losers over 35 years of dragonfly and damselfly distributional change in Germany

2020 ◽  
Author(s):  
D.E Bowler ◽  
D. Eichenberg ◽  
K.J. Conze ◽  
F. Suhling ◽  
K. Baumann ◽  
...  
Keyword(s):  
Citizen Science ◽  
Large Scale ◽  
Positive Trend ◽  
Science Data ◽  
Running Water ◽  
Distributional Change ◽  
Novel Approach ◽  
Occupancy Detection ◽  
Declining Species

AbstractRecent studies suggest insect declines in parts of Europe; however, the generality of these trends across different taxa and regions remains unclear. Standardized data are not available to assess large-scale, long-term changes for most insect groups but opportunistic citizen science data is widespread for some taxa. We compiled over 1 million occurrence records of Odonata (dragonflies and damselflies) from different regional databases across Germany. We used occupancy-detection models to estimate annual distributional changes between 1980 and 2016 for each species. We related species attributes to changes in the species’ distributions and inferred possible drivers of change. Species showing increases were generally warm-adapted species and/or running water species while species showing decreases were cold-adapted species using standing water habitats such as bogs. We developed a novel approach using time-series clustering to identify groups of species with similar patterns of temporal change. Using this method, we defined five typical patterns of change for Odonata – each associated with a specific combination of species attributes. Overall, trends in Odonata provide mixed news – improved water quality, coupled with positive impacts of climate change, could explain the positive trend status of many species. At the same time, declining species point to conservation challenges associated with habitat loss and degradation. Our study demonstrates the great value of citizen science data for assessing large-scale distributional change and conservation decision-making.

scholarly journals Deep learning identification for citizen science surveillance of tiger mosquitoes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Balint Armin Pataki ◽  
Joan Garriga ◽  
Roger Eritja ◽  
John R. B. Palmer ◽  
Frederic Bartumeus ◽  
...  
Keyword(s):  
Deep Learning ◽  
Data Collection ◽  
Citizen Science ◽  
Large Scale ◽  
Mosquito Species ◽  
Characteristic Curve ◽  
Science System ◽  
Public Health Agencies ◽  
Manual Inspection ◽  
Long Run

AbstractGlobal monitoring of disease vectors is undoubtedly becoming an urgent need as the human population rises and becomes increasingly mobile, international commercial exchanges increase, and climate change expands the habitats of many vector species. Traditional surveillance of mosquitoes, vectors of many diseases, relies on catches, which requires regular manual inspection and reporting, and dedicated personnel, making large-scale monitoring difficult and expensive. New approaches are solving the problem of scalability by relying on smartphones and the Internet to enable novel community-based and digital observatories, where people can upload pictures of mosquitoes whenever they encounter them. An example is the Mosquito Alert citizen science system, which includes a dedicated mobile phone app through which geotagged images are collected. This system provides a viable option for monitoring the spread of various mosquito species across the globe, although it is partly limited by the quality of the citizen scientists’ photos. To make the system useful for public health agencies, and to give feedback to the volunteering citizens, the submitted images are inspected and labeled by entomology experts. Although citizen-based data collection can greatly broaden disease-vector monitoring scales, manual inspection of each image is not an easily scalable option in the long run, and the system could be improved through automation. Based on Mosquito Alert’s curated database of expert-validated mosquito photos, we trained a deep learning model to find tiger mosquitoes (Aedes albopictus), a species that is responsible for spreading chikungunya, dengue, and Zika among other diseases. The highly accurate 0.96 area under the receiver operating characteristic curve score promises not only a helpful pre-selector for the expert validation process but also an automated classifier giving quick feedback to the app participants, which may help to keep them motivated. In the paper, we also explored the possibilities of using the model to improve future data collection quality as a feedback loop.

scholarly journals Latitudinal variation in arrival and breeding phenology of the pied flycatcher Ficedula hypoleuca using large‐scale citizen science data

2021 ◽  
Vol 52 (2) ◽  
Author(s):  
Pedro G. Nicolau ◽  
Malcolm D. Burgess ◽  
Tiago A. Marques ◽  
Stephen R. Baillie ◽  
Nick J. Moran ◽  
...  
Keyword(s):  
Citizen Science ◽  
Large Scale ◽  
Ficedula Hypoleuca ◽  
Pied Flycatcher ◽  
Latitudinal Variation ◽  
Breeding Phenology ◽  
Science Data

scholarly journals Comparison of large‐scale citizen science data and long‐term study data for phenology modeling

Ecology ◽  
2018 ◽  
Vol 100 (2) ◽  
pp. e02568 ◽  
Author(s):  
Shawn D. Taylor ◽  
Joan M. Meiners ◽  
Kristina Riemer ◽  
Michael C. Orr ◽  
Ethan P. White
Keyword(s):  
Citizen Science ◽  
Large Scale ◽  
Study Data ◽  
Science Data ◽  
Term Study ◽  
Long Term Study

Estimating conservation metrics from atlas data: the case of southern African endemic birds

2017 ◽  
Vol 27 (3) ◽  
pp. 323-336 ◽  
Author(s):  
ALAN T. K. LEE ◽  
RES ALTWEGG ◽  
PHOEBE BARNARD
Keyword(s):  
Citizen Science ◽  
Large Scale ◽  
Population Change ◽  
Conservation Status ◽  
Bird Species ◽  
Science Data ◽  
Atlas Data ◽  
Endemic Birds ◽  
Change Metrics ◽  
Threatened Status

SummaryThe robust assessment of conservation status increasingly requires population metrics for species that may be little-researched, with no prospect of immediate improvement, but for which citizen science atlas data may exist. We explore the potential for bird atlas data to generate population metrics of use in red data assessment, using the endemic and near-endemic birds of southern Africa. This region, defined here as South Africa, Lesotho and Swaziland, is home to a large number of endemic bird species and an active atlas project. The Southern African Bird Atlas Projects (SABAP) 1 and 2 are large-scale citizen science data sets, consisting of hundreds of thousands of bird checklists and > 10 million bird occurrence records on a grid across the subcontinent. These data contain detailed information on species’ distributions and population change. For conservationists, metrics that guide decisions on the conservation status of a species for red listing can be obtained from SABAP, including range size, range change, population change, and range connectivity (fragmentation). We present a range of conservation metrics for these bird species, focusing on population change metrics together with an associated statistical confidence metric. Population change metrics correlate with change metrics calculated from dynamic occupancy modelling for a set of 191 common species. We identify four species with neither international nor local threatened status, yet for which bird atlas data suggest alarming declines, and two species with threatened status for which our metrics suggest could be reconsidered. A standardised approach to deciding the conservation status of a species is useful so that charismatic or flagship species do not receive disproportionate attention, although ultimately conservation status of any species must always be a consultative process.

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