Using citizen science data to assess the difference in marine debris loads on reefs in Queensland, Australia

AbstractCitizen science platforms are quickly accumulating hundreds of millions of biodiversity observations around the world annually. Quantifying and correcting for the biases in citizen science datasets remains an important first step before these data are used to address ecological questions and monitor biodiversity. One source of potential bias among datasets is the difference between those citizen science programs that have unstructured protocols and those that have semi-structured or structured protocols for submitting observations. To quantify biases in an unstructured citizen science platform, we contrasted bird observations from the unstructured iNaturalist platform with that from a semi-structured citizen science platform—eBird—for the continental United States. We tested whether four traits of species (body size, commonness, flock size, and color) predicted if a species was under- or over-represented in the unstructured dataset compared with the semi-structured dataset. We found strong evidence that large-bodied birds were over-represented in the unstructured citizen science dataset; moderate evidence that common species were over-represented in the unstructured dataset; strong evidence that species in large groups were over-represented; and no evidence that colorful species were over-represented in unstructured citizen science data. Our results suggest that biases exist in unstructured citizen science data when compared with semi-structured data, likely as a result of the detectability of a species and the inherent recording process. Importantly, in programs like iNaturalist the detectability process is two-fold—first, an individual organism needs to be detected, and second, it needs to be photographed, which is likely easier for many large-bodied species. Our results indicate that caution is warranted when using unstructured citizen science data in ecological modelling, and highlight body size as a fundamental trait that can be used as a covariate for modelling opportunistic species occurrence records, representing the detectability or identifiability in unstructured citizen science datasets. Future research in this space should continue to focus on quantifying and documenting biases in citizen science data, and expand our research by including structured citizen science data to understand how biases differ among unstructured, semi-structured, and structured citizen science platforms.

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Comparison of marine debris data collected by researchers and citizen scientists: Is citizen science data worth the effort?

Biological Conservation ◽

10.1016/j.biocon.2016.05.025 ◽

2017 ◽

Vol 208 ◽

pp. 127-138 ◽

Cited By ~ 37

Author(s):

Tonya van der Velde ◽

David A. Milton ◽

T.J. Lawson ◽

Chris Wilcox ◽

Matt Lansdell ◽

...

Keyword(s):

Citizen Science ◽

Marine Debris ◽

Science Data ◽

Data Worth

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Large-bodied birds are over-represented in opportunistic citizen science data

10.32942/osf.io/vnspb ◽

2021 ◽

Author(s):

Corey Thomas Callaghan ◽

Ailstair G. B. Poore ◽

Max Hofmann ◽

Christopher Roberts ◽

Henrique Pereira

Keyword(s):

Citizen Science ◽

Common Species ◽

Future Research ◽

Color Group ◽

Science Data ◽

Moderate Evidence ◽

Recording Process ◽

The Difference ◽

Implicit And Explicit ◽

Large Groups

Citizen science platforms are quickly accumulating hundreds of millions of biodiversity observations around the world annually. Quantifying and correcting for the implicit and explicit biases in citizen science datasets remains an important first step before these data are used to address ecological questions and monitor biodiversity. One source of potential bias among datasets is the difference between those citizen science programs that collect opportunistic observations and those that have semi-structured or structured protocols for submitting observations. To quantify biases in an unstructured citizen science platform, we contrasted bird observations from the iNaturalist platform with that from a semi-structured citizen science platform — eBird — for the continental United States. We tested whether four traits of species (color, group size, body size, and commonness) predicted whether a species was over-represented in the opportunistic dataset. We found strong evidence that large-bodied birds were over-represented in the opportunistic citizen science dataset; moderate evidence that common species were over-represented in the opportunistic data; moderate evidence that species in large groups were over-represented; and no evidence that colorful species were over-represented in opportunistic citizen science data. Our results suggest that biases exist in opportunistic citizen science datasets, likely as a result of the detectability of a species and the inherent recording process. Future research in this space should continue to focus on quantifying and documenting biases in citizen science data, and understanding how these biases differ among unstructured, semi-structured, and structured citizen science platforms.

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

Diversity ◽

10.3390/d13070293 ◽

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.

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

Insects ◽

10.3390/insects12080683 ◽

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.

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Explaining Spatial Variation in the Recording Effort of Citizen Science Data across Multiple Taxa

PLoS ONE ◽

10.1371/journal.pone.0147796 ◽

2016 ◽

Vol 11 (1) ◽

pp. e0147796 ◽

Cited By ~ 36

Author(s):

Louise Mair ◽

Alejandro Ruete

Keyword(s):

Spatial Variation ◽

Citizen Science ◽

Science Data

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A Conceptual Probabilistic Framework for Annotation Aggregation of Citizen Science Data

Mathematics ◽

10.3390/math9080875 ◽

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.

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

Sustainability Science ◽

10.1007/s11625-021-01001-1 ◽

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.

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