Three Frontiers for the Future of Biodiversity Research Using Citizen Science Data

BioScience ◽  
2020 ◽  
Author(s):  
Corey T Callaghan ◽  
Alistair G B Poore ◽  
Thomas Mesaglio ◽  
Angela T Moles ◽  
Shinichi Nakagawa ◽  
...  
Keyword(s):  
Citizen Science ◽  
Secondary Data ◽  
Science Research ◽  
Single Species ◽  
Data Sets ◽  
Biodiversity Data ◽  
Science Data ◽  
Biodiversity Research ◽  
Research Areas ◽  
The Future

Abstract Citizen science is fundamentally shifting the future of biodiversity research. But although citizen science observations are contributing an increasingly large proportion of biodiversity data, they only feature in a relatively small percentage of research papers on biodiversity. We provide our perspective on three frontiers of citizen science research, areas that we feel to date have had minimal scientific exploration but that we believe deserve greater attention as they present substantial opportunities for the future of biodiversity research: sampling the undersampled, capitalizing on citizen science's unique ability to sample poorly sampled taxa and regions of the world, reducing taxonomic and spatial biases in global biodiversity data sets; estimating abundance and density in space and time, develop techniques to derive taxon-specific densities from presence or absence and presence-only data; and capitalizing on secondary data collection, moving beyond data on the occurrence of single species and gain further understanding of ecological interactions among species or habitats. The contribution of citizen science to understanding the important biodiversity questions of our time should be more fully realized.

scholarly journals Notes of Life: A platform for recording species observations driven by artificial intelligence

2019 ◽  
Vol 3 ◽  
Author(s):  
Congtian Lin ◽  
Jiangning Wang ◽  
Liqiang Ji
Keyword(s):  
Artificial Intelligence ◽  
Citizen Science ◽  
Higher Plants ◽  
Mobile App ◽  
Biodiversity Data ◽  
Science Data ◽  
Biodiversity Research ◽  
Large Numbers ◽  
In The Wild ◽  
Speed Up

Biodiversity research is stepping into a big data era with the rapid increase in the abundance of biodiversity data, especially the large number of species images. It has been a new trend and hot topic on how to utilize artificial intelligence to mine big biodiversity data to support wildlife observation and recognition. In this research, we integrate large numbers of species images, including higher plants, birds and insects, and use a state-of-the-art image deep learning technique to train species auto-recognition models. Currently, we get a model that can recognize more than 900 Chinese birds with top 1 accuracy 81% and top 5 accuracy 95% (top n accuracy means the probability that the correct answer presents in top n predicted results), and more models are coming soon. Based on these models, we developed a platform named Notes of Life (NOL, http://nol.especies.cn), which includes a website and a mobile application (app) for assisting biological scientists and citizen scientists to recognize and record wildlife. Users can upload their observation records and images of wildlife through our mobile app while they are investigating in the wild. The website is used for bulk data uploading and management. Species images can be classified by taxon-specific, plug-in recognition models that speed up the process of identification. There is an expert module in NOL where citizen scientists can work interactively with information provided by biological scientists, and post a species image identification request to experts when they cannot recognize the species by themselves or from models. The expert module is for improving the quality of citizen science data, and it is a supplement of the disadvantage of species auto-recognition models. Above all, NOL embraces the idea that scientific research supports citizen science and citizen science gives feedback to science, and of finding a sustainable way to collect increasingly more reliable data for biodiversity research.

scholarly journals TrIAS, leveraging citizen science data to monitor invasive species in Belgium

2018 ◽  
Vol 2 ◽  
pp. e24749
Author(s):  
Quentin Groom ◽  
Tim Adriaens ◽  
Damiano Oldoni ◽  
Lien Reyserhove ◽  
Diederik Strubbe ◽  
...  
Keyword(s):  
Invasive Species ◽  
Alien Species ◽  
Citizen Science ◽  
Invasive Alien Species ◽  
Science Research ◽  
Open Science ◽  
Policy Support ◽  
Science Data ◽  
Common View ◽  
Indispensable Tool

Reducing the damage caused by invasive species requires a community approach informed by rapidly mobilized data. Even if local stakeholders work together, invasive species do not respect borders, and national, continental and global policies are required. Yet, in general, data on invasive species are slow to be mobilized, often of insufficient quality for their intended application and distributed among many stakeholders and their organizations, including scientists, land managers, and citizen scientists. The Belgian situation is typical. We struggle with the fragmentation of data sources and restrictions to data mobility. Nevertheless, there is a common view that the issue of invasive alien species needs to be addressed. In 2017 we launched the Tracking Invasive Alien Species (TrIAS) project, which envisages a future where alien species data are rapidly mobilized, the spread of exotic species is regularly monitored, and potential impacts and risks are rapidly evaluated in support of policy decisions (Vanderhoeven et al. 2017). TrIAS is building a seamless, data-driven workflow, from raw data to policy support documentation. TrIAS brings together 21 different stakeholder organizations that covering all organisms in the terrestrial, freshwater and marine environments. These organizations also include those involved in citizen science, research and wildlife management. TrIAS is an Open Science project and all the software, data and documentation are being shared openly (Groom et al. 2018). This means that the workflow can be reused as a whole or in part, either after the project or in different countries. We hope to prove that rapid data workflows are not only an indispensable tool in the control of invasive species, but also for integrating and motivating the citizens and organizations involved.

scholarly journals A Framework of Observer-Based Biases in Citizen Science Biodiversity Monitoring: Semi-Structuring Unstructured Biodiversity Monitoring Protocols

2021 ◽  
Vol 9 ◽  
Author(s):  
Ofer Arazy ◽  
Dan Malkinson
Keyword(s):  
Decision Making ◽  
Citizen Science ◽  
Temporal Distribution ◽  
Biodiversity Monitoring ◽  
Biodiversity Data ◽  
Science Data ◽  
The Public ◽  
Spatio Temporal ◽  
Monitoring Protocols ◽  
And Behavior

Citizen science, whereby ordinary citizens participate in scientific endeavors, is widely used for biodiversity monitoring, most commonly by relying on unstructured monitoring approaches. Notwithstanding the potential of unstructured citizen science to engage the public and collect large amounts of biodiversity data, observers’ considerations regarding what, where and when to monitor result in biases in the aggregate database, thus impeding the ability to draw conclusions about trends in species’ spatio-temporal distribution. Hence, the goal of this study is to enhance our understanding of observer-based biases in citizen science for biodiversity monitoring. Toward this goals we: (a) develop a conceptual framework of observers’ decision-making process along the steps of monitor – > record and share, identifying the considerations that take place at each step, specifically highlighting the factors that influence the decisions of whether to record an observation (b) propose an approach for operationalizing the framework using a targeted and focused questionnaire, which gauges observers’ preferences and behavior throughout the decision-making steps, and (c) illustrate the questionnaire’s ability to capture the factors driving observer-based biases by employing data from a local project on the iNaturalist platform. Our discussion highlights the paper’s theoretical contributions and proposes ways in which our approach for semi-structuring unstructured citizen science data could be used to mitigate observer-based biases, potentially making the collected biodiversity data usable for scientific and regulatory purposes.

scholarly journals An Introduction to Bayesian Inference via Variational Approximations

2011 ◽  
Vol 19 (1) ◽  
pp. 32-47 ◽  
Author(s):  
Justin Grimmer
Keyword(s):  
Bayesian Inference ◽  
Political Science ◽  
Bayesian Models ◽  
Large Data ◽  
Science Research ◽  
Mcmc Methods ◽  
Data Sets ◽  
Science Data ◽  
Variational Approximations ◽  
Fully Bayesian

Markov chain Monte Carlo (MCMC) methods have facilitated an explosion of interest in Bayesian methods. MCMC is an incredibly useful and important tool but can face difficulties when used to estimate complex posteriors or models applied to large data sets. In this paper, we show how a recently developed tool in computer science for fitting Bayesian models, variational approximations, can be used to facilitate the application of Bayesian models to political science data. Variational approximations are often much faster than MCMC for fully Bayesian inference and in some instances facilitate the estimation of models that would be otherwise impossible to estimate. As a deterministic posterior approximation method, variational approximations are guaranteed to converge and convergence is easily assessed. But variational approximations do have some limitations, which we detail below. Therefore, variational approximations are best suited to problems when fully Bayesian inference would otherwise be impossible. Through a series of examples, we demonstrate how variational approximations are useful for a variety of political science research. This includes models to describe legislative voting blocs and statistical models for political texts. The code that implements the models in this paper is available in the supplementary material.

scholarly journals Improving estimation of species distribution from citizen-science records using data-integration models

2021 ◽  
Author(s):  
Viviane Zulian ◽  
David A. W. Miller ◽  
Goncalo Ferraz
Keyword(s):  
Data Integration ◽  
Spatial Autocorrelation ◽  
Citizen Science ◽  
Geographic Range ◽  
Sampling Effort ◽  
Data Sets ◽  
Science Data ◽  
Integration Model ◽  
Data Set ◽  
Joint Likelihood

Mapping species distributions is a crucial but challenging requirement of wildlife management. The frequent need to sample vast expanses of potential habitat increases the cost of planned surveys and rewards accumulation of opportunistic observations. In this paper, we integrate planned survey data from roost counts with opportunistic samples from eBird, WikiAves and Xeno-canto citizen-science platforms to map the geographic range of the endangered Vinaceous-breasted Parrot. We demonstrate the estimation and mapping of species occurrence based on data integration while accounting for specifics of each data set, including observation technique and uncertainty about the observations. Our analysis illustrates 1) the incorporation of sampling effort, spatial autocorrelation, and site covariates in a joint-likelihood, hierarchical, data-integration model; 2) the evaluation of the contribution of each data set, as well as the contribution of effort covariates, spatial autocorrelation, and site covariates to the predictive ability of fitted models using a cross-validation approach; and 3) how spatial representation of the latent occupancy state (i.e. realized occupancy) helps identify areas with high uncertainty that should be prioritized in future field work. Our results reveal a Vinaceous-breasted Parrot geographic range of 434,670 square kilometers, which is three times larger than the Extant area previously reported in the IUCN Red List. The exclusion of one data set at a time from the analyses always resulted in worse predictions by the models of truncated data than by the full model, which included all data sets. Likewise, exclusion of spatial autocorrelation, site covariates, or sampling effort resulted in worse predictions. The integration of different data sets into one joint-likelihood model produced a more reliable representation of the species range than any individual data set taken on its own improving the use of citizen science data in combination with planned survey results.

scholarly journals Evaluating the Fitness for Use of Citizen Science Data for Wildlife Monitoring

2021 ◽  
Vol 9 ◽  
Author(s):  
Heather A. Fischer ◽  
Leah R. Gerber ◽  
Elizabeth A. Wentz
Keyword(s):  
Data Quality ◽  
Citizen Science ◽  
Ecological Monitoring ◽  
Global Scale ◽  
Third Party ◽  
Data Sets ◽  
Science Data ◽  
Spatial And Temporal Scales ◽  
Fine Grained ◽  
Assessment Approach

Contributory citizen science programs focused on ecological monitoring can produce fine-grained and expansive data sets across spatial and temporal scales. With this data collection potential, citizen scientists can significantly impact the ability to monitor ecological patterns. However, scientists still harbor skepticism about using citizen science data in their work, generally due to doubts about data quality. Numerous peer-reviewed articles have addressed data quality in citizen science. Yet, many of these methods are not useable by third-party scientists (scientists who are not directly involved in the citizen science program). In addition, these methods generally capture internal data quality rather than a dataset’s potential to be used for a specific purpose. Assessing data fitness for use represents a promising approach to evaluating data accuracy and quality for different applications and contexts. In this article, we employ a Spatial, Temporal, Aptness, and Application (STAAq) assessment approach to assess data fitness for use of citizen science datasets. We tested the STAAq assessment approach through a case study examining the distribution of caribou in Denali National Park and Preserve. Three different datasets were used in the test, Map of Life data (a global scale citizen science mobile application for recording species observations), Ride Observe and Record data (a program sponsored by the park staff where incentivized volunteers observe species in the park), and conventionally collected radio collar data. The STAAq assessment showed that the Map of Life and Ride Observe and Record program data are fit for monitoring caribou distribution in the park. This data fitness for use approach is a promising way to assess the external quality of a dataset and its fitness to address particular research or monitoring questions. This type of assessment may help citizen science skeptics see the value and potential of citizen science collected data and encourage the use of citizen science data by more scientists.

scholarly journals Advancing Citizen Science for Coastal and Ocean Research

2019 ◽  
Author(s):  
Carlos Garcia-Soto ◽  
Gro I. van der Meeren ◽  
Jane Delany ◽  
Christine Domegan ◽  
Karin Dubsky ◽  
...  
Keyword(s):  
Citizen Science ◽  
Future Development ◽  
Good Practice ◽  
Science Research ◽  
Natural World ◽  
Marine Policy ◽  
Data Types ◽  
Marine Research ◽  
The Future ◽  
High Level

n Citizen Science, members of the general public collaborate with scientists to generate and use data relating to the natural world. For the many fields of marine research, this is a particularly powerful approach which should not be overlooked. The sheer scale of coastal and ocean environments mean that it would take several lifetimes for scientists to study them alone. By collaborating with citizens, a much greater number of people can be mobilized to gather a wealth of data and develop new scientific knowledge and understanding. The variety of data types which are amenable to Citizen Science, as outlined in the position paper, are great, meaning that there could be a project to suit everyone. Citizen Science can also enable participants to improve their Ocean Literacy, gain new skills and experiences, and can also empower them to participate in the process of delivering future marine policy.Now, more than ever, marine science research is needed to understand the impacts of a world undergoing change. The rise of Marine Citizen Science to help address this need is therefore timely. This paper highlights opportunities, challenges and best practice in Marine Citizen Science, and sets out a list of high-level strategic recommendations for the future development of Marine Citizen Science in Europe. It presents examples of existing Marine Citizen Science initiatives in Europe to illustrate good practice. Common concerns such as data quality and maintaining engagement are discussed, as are future opportunities such as increased use of technology and potential role of Marine Citizen Science in informing marine policy and conservation. The paper closes with a list of high-level strategic recommendations for the future development of Marine Citizen Science in Europe.

scholarly journals Visualising reporting patterns of Citizen Science observations and their importance for the interpretation of data and visualisations: the example of ornithological observations

2020 ◽  
Vol 2 ◽  
pp. 1-1
Author(s):  
Jana Moser ◽  
Johannes Wahl ◽  
Stephan Schwan ◽  
Julia Moritz ◽  
Tom Hoyer ◽  
...  
Keyword(s):  
Citizen Science ◽  
Data Transfer ◽  
Regional Distribution ◽  
Bird Species ◽  
Distribution Patterns ◽  
Data Sets ◽  
Great Effort ◽  
Science Data ◽  
Advantages And Disadvantages ◽  
Data Collections

Abstract. Every day, nature enthusiasts collect valuable information about local animal and plant populations. This has resulted in approximately 50 million data sets until now for the bird observations at ornitho.de alone, with the observation numbers having risen sharply during the last three years. Such data may not always meet all scientific criteria, but due to their quantity they allow statements to be made, e.g. about regional distribution patterns or local occurrences. However, the citizens involved in the survey and the interested public are still unable to use these data, or can only use them in very low resolution or at great effort. According to the current Citizen Science Guidelines, data collecting citizens and other interested parties should be able to use such data and, for example, discover the ornithological diversity on their own doorstep. For this reason, the “Experiencing biodiversity” project has developed web maps, which, by the way, should inspire even more people to record biodiversity.The presentation will firstly address the visualization and interpretation of citizen science data collections. In order to create awareness that numerous animal and plant species are in danger and need special protection, an overview of the current species inventory is necessary. The aim of the project is to provide this overview by visual means to the participating citizen scientists but also to the interested public. The maps should be easy to understand and take into account the varying degrees of citizen science participation in different regions. Thus, in addition to the occurrence of a bird species, the reporting activity is important and therefore displayed on the map. This is unique and shows how many reports are available for all bird species recorded. Thus, it is possible to estimate the amount and distribution of the available data and to compare them with the reported occurrence of a single bird species. As a result, data gaps or changes over time become visible and support the interpretation of the data. As there are no negative reports, only this additional level allows an interpretation at all whether a bird species is "not present" or whether it was just "not reported" in certain areas. At the same time, the project wants to contribute to a better understanding of the visualizations, but also of the underlying data basis.Secondly, the presentation will also address the sometimes contradictory relationship between visualization – i.e. making visible – and the most diverse interests of protection – i.e. avoiding making visible. And thirdly, challenges in the visualization of mass data in the sense of a performant presentation of such data to a broad public will be discussed. A special challenge with regard to performance issues are rapidly increasing data volumes and questions of data protection, data transfer to the visualization but also permanent costs for the provision of data which should not be underestimated.Due to the ongoing status of the project, interim results are presented and mirrored with currently ongoing scientific studies on the use of the maps by Citizen Scientists and the influence of the visualisations on the motivation of ornithologists. Advantages and disadvantages of this visualisation will be discussed with regard to its transferability to other projects.

scholarly journals Using iNaturalist to Contribute Your Nature Observations to Science

EDIS ◽  
2019 ◽  
Vol 2019 (4) ◽  
pp. 5
Author(s):  
Matthew Earl Boone ◽  
Mathieu Basille
Keyword(s):  
Citizen Science ◽  
Online Community ◽  
Science Data ◽  
Online Data ◽  
Wildlife Ecology ◽  
Biodiversity Research ◽  
The World ◽  
The Rich ◽  
Online Data Base ◽  
Community Projects

iNaturalist is one of the most popular citizen science data portals in the world. Citizens can submit pictures of biological observations to an online data base to be reviewed by the rich online community and used for important biodiversity research around the world. Users can use the iNaturalist ap to plan community projects and bioblitzes and learn more about species identification and biodiversity. In this 5-page fact sheet, authors Matthew Earl Boone and Mathieu Basille explain how observations are vetted and used and give a step by step guide to get started! Published by the UF/IFAS Department of Wildlife Ecology and Conservation. https://edis.ifas.ufl.edu/uw458

scholarly journals Perspective: The Power (Dynamics) of Open Data in Citizen Science

2021 ◽  
Vol 3 ◽  
Author(s):  
Caren B. Cooper ◽  
Lisa M. Rasmussen ◽  
Elizabeth D. Jones
Keyword(s):  
Citizen Science ◽  
Ethical Issues ◽  
Open Data ◽  
Power Dynamics ◽  
Data Sets ◽  
Data Governance ◽  
Science Data ◽  
Data Set ◽  
Ethical Decisions ◽  
The Relationship

In citizen science, data stewards and data producers are often not the same people. When those who have labored on data collection are not in control of the data, ethical problems could arise from this basic structural feature. In this Perspective, we advance the proposition that stewarding data sets generated by volunteers involves the typical technical decisions in conventional research plus a suite of ethical decisions stemming from the relationship between professionals and volunteers. Differences in power, priorities, values, and vulnerabilities are features of the relationship between professionals and volunteers. Thus, ethical decisions about open data practices in citizen science include, but are not limited to, questions grounded in respect for volunteers: who decides data governance structures, who receives attribution for a data set, which data are accessible and to whom, and whose interests are served by the data use/re-use. We highlight ethical issues that citizen science practitioners should consider when making data governance decisions, particularly with respect to open data.

Sign in / Sign up

Export Citation Format

Share Document