Using citizen science to study exotic and invasive birds.

2020 ◽  
pp. 363-368
Author(s):  
Corey T. Callaghan ◽  
Daniel M. Brooks
Keyword(s):  
Citizen Science ◽  
Current Knowledge ◽  
Science Data ◽  
Bird Populations ◽  
Science Projects ◽  
Current Usage ◽  
Exotic Birds ◽  
Introduced Birds ◽  
Types Of Information ◽  
Invasive Birds

Abstract This chapter describes the current usage of citizen science to increase the current knowledge of exotic and invasive bird populations. Citizen science projects aimed at studying and tracking exotic birds are divided into two categories: those that are part of a broad-scale biodiversity collection scheme, and those that are species focused. Some of the current literature that integrates citizen science data and the effects, management and implications of introduced birds are summarized, and select examples that highlight the potential of citizen science aimed at gathering various types of information about exotic birds are included. A vision for the future of citizen science aimed at studying exotic birds is also discussed.

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 Analytical guidelines to increase the value of citizen science data: using eBird data to estimate species occurrence

2019 ◽  
Author(s):  
A Johnston ◽  
WM Hochachka ◽  
ME Strimas-Mackey ◽  
V Ruiz Gutierrez ◽  
OJ Robinson ◽  
...  
Keyword(s):  
Data Processing ◽  
Citizen Science ◽  
Model Performance ◽  
Species Distributions ◽  
Ecological Knowledge ◽  
Sample Sizes ◽  
Science Data ◽  
Science Projects ◽  
Wide Range ◽  
The Impact

AbstractCitizen science data are valuable for addressing a wide range of ecological research questions, and there has been a rapid increase in the scope and volume of data available. However, data from large-scale citizen science projects typically present a number of challenges that can inhibit robust ecological inferences. These challenges include: species bias, spatial bias, and variation in effort.To demonstrate addressing key challenges in analysing citizen science data, we use the example of estimating species distributions with data from eBird, a large semi-structured citizen science project. We estimate two widely applied metrics of species distributions: encounter rate and occupancy probability. For each metric, we assess the impact of data processing steps that either degrade or refine the data used in the analyses. We also test whether differences in model performance are maintained at different sample sizes.Model performance improved when data processing and analytical methods addressed the challenges arising from citizen science data. The largest gains in model performance were achieved with: 1) the use of complete checklists (where observers report all the species they detect and identify); and 2) the use of covariates describing variation in effort and detectability for each checklist. Occupancy models were more robust to a lack of complete checklists and effort variables. Improvements in model performance with data refinement were more evident with larger sample sizes.Here, we describe processes to refine semi-structured citizen science data to estimate species distributions. We demonstrate the value of complete checklists, which can inform the design and adaptation of citizen science projects. We also demonstrate the value of information on effort. The methods we have outlined are also likely to improve other forms of inference, and will enable researchers to conduct robust analyses and harness the vast ecological knowledge that exists within citizen science data.

scholarly journals Utilizing citizen science to model the distribution of Aedes aegypti in West Africa

2022 ◽  
Author(s):  
Elizabeth A. Freeman ◽  
Elizabeth J. Carlton ◽  
Sara Paull ◽  
Samuel Dadzie ◽  
Andrea Buchwald
Keyword(s):  
Public Health ◽  
West Africa ◽  
Citizen Science ◽  
Mosquito Control ◽  
Current Knowledge ◽  
Disease Vectors ◽  
Aedes Mosquito ◽  
Vector Surveillance ◽  
Science Data ◽  
Aedes Mosquitoes

In a rapidly urbanizing region such as West Africa, Aedes mosquitoes pose an emerging threat of infectious disease that is compounded by limited vector surveillance. Citizen science has been proposed as a way to fill surveillance gaps by training local residents to collect and share information on disease vectors. Increasing citizen science efforts can begin to bridge the gaps in our current knowledge of Aedes distribution while engaging locals with mosquito control and public health efforts. Understanding the distribution of disease vectors in West Africa can inform researchers and public health officials on where to conduct disease surveillance and focus public health interventions. We aimed to compare citizen science data to published literature observations of Aedes mosquitoes and to quantify how incorporating citizen science changes our understanding of Aedes mosquito distribution in West Africa. We utilized citizen science data collected through NASAs GLOBE Observer mobile phone application and data from a previously published literature review on Aedes mosquito distribution to examine the contribution of citizen science to understanding the distribution of Ae. aegypti in West Africa using Maximum Entropy modeling. Combining citizen science and literature-derived observations improved the fit of the model compared to models created by each data source alone, but did not alleviate location bias within the models, likely due to lack of widespread observations. Understanding Ae. aegypti distribution will require greater investment in Aedes mosquito surveillance in the region, and citizen science should be utilized as a tool in this mission to increase the reach of surveillance.

scholarly journals Citizens, Scientists, and Enablers: A Tripartite Model for Citizen Science Projects

Diversity ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 309
Author(s):  
Rhian A. Salmon ◽  
Samuel Rammell ◽  
Myfanwy T. Emeny ◽  
Stephen Hartley
Keyword(s):  
Case Studies ◽  
Nature Of Science ◽  
Citizen Science ◽  
Public Engagement ◽  
Science Communication ◽  
Tripartite Model ◽  
Science Project ◽  
Science Projects ◽  
Emerging Issues ◽  
Citizen Science Project

In this paper, we focus on different roles in citizen science projects, and their respective relationships. We propose a tripartite model that recognises not only citizens and scientists, but also an important third role, which we call the ‘enabler’. In doing so, we acknowledge that additional expertise and skillsets are often present in citizen science projects, but are frequently overlooked in associated literature. We interrogate this model by applying it to three case studies and explore how the success and sustainability of a citizen science project requires all roles to be acknowledged and interacting appropriately. In this era of ‘wicked problems’, the nature of science and science communication has become more complex. In order to address critical emerging issues, a greater number of stakeholders are engaging in multi-party partnerships and research is becoming increasingly interdisciplinary. Within this context, explicitly acknowledging the role and motivations of everyone involved can provide a framework for enhanced project transparency, delivery, evaluation and impact. By adapting our understanding of citizen science to better recognise the complexity of the organisational systems within which they operate, we propose an opportunity to strengthen the collaborative delivery of both valuable scientific research and public engagement.

scholarly journals The Use of Citizen Science to Achieve Multivariate Management Goals on Public Lands

Diversity ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 293
Author(s):  
Sara Souther ◽  
Vincent Randall ◽  
Nanebah Lyndon
Keyword(s):  
Citizen Science ◽  
Land Management ◽  
Ecological Integrity ◽  
Public Land ◽  
Suitable Habitat ◽  
Science Data ◽  
Public Land Management ◽  
Federal Land ◽  
Botanical Research ◽  
Federal Land Management

Federal land management agencies in the US are tasked with maintaining the ecological integrity of over 2 million km2 of land for myriad public uses. Citizen science, operating at the nexus of science, education, and outreach, offers unique benefits to address socio-ecological questions and problems, and thus may offer novel opportunities to support the complex mission of public land managers. Here, we use a case study of an iNaturalist program, the Tribal Nations Botanical Research Collaborative (TNBRC), to examine the use of citizen science programs in public land management. The TNBRC collected 2030 observations of 34 plant species across the project area, while offering learning opportunities for participants. Using occurrence data, we examined observational trends through time and identified five species with 50 or fewer digital observations to investigate as species of possible conservation concern. We compared predictive outcomes of habitat suitability models built using citizen science data and Forest Inventory and Analysis (FIA) data. Models exhibited high agreement, identifying the same underlying predictors of species occurrence and, 95% of the time, identifying the same pixels as suitable habitat. Actions such as staff training on data use and interpretation could enhance integration of citizen science in Federal land management.

scholarly journals Abundant Citizen Science Data Reveal That the Peacock Butterfly Aglais io Recently Became Bivoltine in Belgium

Insects ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 683
Author(s):  
Marc Herremans ◽  
Karin Gielen ◽  
Jos Van Kerckhoven ◽  
Pieter Vanormelingen ◽  
Wim Veraghtert ◽  
...  
Keyword(s):  
Citizen Science ◽  
Western Europe ◽  
Environmental Changes ◽  
Early Summer ◽  
Science Data ◽  
The Public ◽  
Online Portal ◽  
Spring Generation ◽  
Reproductive Cycles ◽  
New Generation

The peacock butterfly is abundant and widespread in Europe. It is generally believed to be univoltine (one generation per year): adults born in summer overwinter and reappear again in spring to reproduce. However, recent flight patterns in western Europe mostly show three peaks during the year: a first one in spring (overwintering butterflies), a second one in early summer (offspring of the spring generation), and a third one in autumn. It was thus far unclear whether this autumn flight peak was a second new generation or consisted of butterflies flying again in autumn after a summer rest (aestivation). The life cycle of one of Europe’s most common butterflies is therefore still surprisingly inadequately understood. We used hundreds of thousands of observations and thousands of pictures submitted by naturalists from the public to the online portal observation.orgin Belgium and analyzed relations between flight patterns, condition (wear), reproductive cycles, peak abundances, and phenology to clarify the current life history. We demonstrate that peacocks have shifted towards two new generations per year in recent decades. Mass citizen science data in online portals has become increasingly important in tracking the response of biodiversity to rapid environmental changes such as climate change.

scholarly journals A Conceptual Probabilistic Framework for Annotation Aggregation of Citizen Science Data

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 875
Author(s):  
Jesus Cerquides ◽  
Mehmet Oğuz Mülâyim ◽  
Jerónimo Hernández-González ◽  
Amudha Ravi Shankar ◽  
Jose Luis Fernandez-Marquez
Keyword(s):  
Data Quality ◽  
Citizen Science ◽  
Graphical Model ◽  
Real Life ◽  
Scientific Journals ◽  
Probabilistic Framework ◽  
Science Data ◽  
Label Aggregation ◽  
Evaluation Of Data ◽  
Model Formalism

Over the last decade, hundreds of thousands of volunteers have contributed to science by collecting or analyzing data. This public participation in science, also known as citizen science, has contributed to significant discoveries and led to publications in major scientific journals. However, little attention has been paid to data quality issues. In this work we argue that being able to determine the accuracy of data obtained by crowdsourcing is a fundamental question and we point out that, for many real-life scenarios, mathematical tools and processes for the evaluation of data quality are missing. We propose a probabilistic methodology for the evaluation of the accuracy of labeling data obtained by crowdsourcing in citizen science. The methodology builds on an abstract probabilistic graphical model formalism, which is shown to generalize some already existing label aggregation models. We show how to make practical use of the methodology through a comparison of data obtained from different citizen science communities analyzing the earthquake that took place in Albania in 2019.

scholarly journals Using citizen science data to monitor the Sustainable Development Goals: a bottom-up analysis

2021 ◽  
Author(s):  
Laura Ballerini ◽  
Sylvia I. Bergh
Keyword(s):  
Sustainable Development ◽  
Citizen Science ◽  
Sustainable Development Goals ◽  
Comparative Case Study ◽  
Science Data ◽  
The Sustainable Development ◽  
Case Study Analysis ◽  
Wide Range ◽  
Development Goals

AbstractOfficial data are not sufficient for monitoring the United Nations Sustainable Development Goals (SDGs): they do not reach remote locations or marginalized populations and can be manipulated by governments. Citizen science data (CSD), defined as data that citizens voluntarily gather by employing a wide range of technologies and methodologies, could help to tackle these problems and ultimately improve SDG monitoring. However, the link between CSD and the SDGs is still understudied. This article aims to develop an empirical understanding of the CSD-SDG link by focusing on the perspective of projects which employ CSD. Specifically, the article presents primary and secondary qualitative data collected on 30 of these projects and an explorative comparative case study analysis. It finds that projects which use CSD recognize that the SDGs can provide a valuable framework and legitimacy, as well as attract funding, visibility, and partnerships. But, at the same time, the article reveals that these projects also encounter several barriers with respect to the SDGs: a widespread lack of knowledge of the goals, combined with frustration and political resistance towards the UN, may deter these projects from contributing their data to the SDG monitoring apparatus.

scholarly journals The impact of data quality filtering of opportunistic citizen science data on species distribution model performance

2021 ◽  
Vol 444 ◽  
pp. 109453
Author(s):  
Camille Van Eupen ◽  
Dirk Maes ◽  
Marc Herremans ◽  
Kristijn R.R. Swinnen ◽  
Ben Somers ◽  
...  
Keyword(s):  
Data Quality ◽  
Citizen Science ◽  
Species Distribution ◽  
Species Distribution Model ◽  
Model Performance ◽  
Distribution Model ◽  
Science Data ◽  
Quality Filtering ◽  
The Impact
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