Incorporating citizen science data in spatially explicit integrated population models

Ecology ◽  
2019 ◽  
Vol 100 (9) ◽  
Author(s):  
Catherine C. Sun ◽  
J. Andrew Royle ◽  
Angela K. Fuller
Keyword(s):  
Citizen Science ◽  
Population Models ◽  
Spatially Explicit ◽  
Science Data

Using citizen science data in integrated population models to inform conservation

2018 ◽  
Vol 227 ◽  
pp. 361-368 ◽  
Author(s):  
Orin J. Robinson ◽  
Viviana Ruiz-Gutierrez ◽  
Daniel Fink ◽  
Robert J. Meese ◽  
Marcel Holyoak ◽  
...  
Keyword(s):  
Citizen Science ◽  
Population Models ◽  
Science Data

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

Weekend bias in Citizen Science data reporting: implications for phenology studies

2012 ◽  
Vol 57 (5) ◽  
pp. 715-720 ◽  
Author(s):  
Jason R. Courter ◽  
Ron J. Johnson ◽  
Claire M. Stuyck ◽  
Brian A. Lang ◽  
Evan W. Kaiser
Keyword(s):  
Citizen Science ◽  
Science Data ◽  
Data Reporting

Uncertainty in spatially explicit population models

2008 ◽  
Vol 141 (4) ◽  
pp. 956-970 ◽  
Author(s):  
E.S. Minor ◽  
R.I. McDonald ◽  
E.A. Treml ◽  
D.L. Urban
Keyword(s):  
Population Models ◽  
Spatially Explicit

Using long-term citizen science data to understand distribution and habitat use of an irruptive species

2021 ◽  
pp. 101377
Author(s):  
Anant Deshwal ◽  
Pooja Panwar ◽  
Joseph C. Neal ◽  
Matthew A. Young
Keyword(s):  
Habitat Use ◽  
Citizen Science ◽  
Science Data
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