Response to Horns et al. 2018: ‘Using opportunistic citizen science data to estimate avian population trends’ Biological Conservation 221, 151–159

2018 ◽  
Vol 226 ◽  
pp. 329-330 ◽  
Author(s):  
Frank A. Fogarty ◽  
Martha E. Wohlfeil ◽  
Erica Fleishman
Keyword(s):  
Citizen Science ◽  
Population Trends ◽  
Biological Conservation ◽  
Science Data

scholarly journals Secretarybird Sagittarius serpentarius Population Trends and Ecology: Insights from South African Citizen Science Data

PLoS ONE ◽  
2014 ◽  
Vol 9 (5) ◽  
pp. e96772 ◽  
Author(s):  
Sally D. Hofmeyr ◽  
Craig T. Symes ◽  
Leslie G. Underhill
Keyword(s):  
South African ◽  
Citizen Science ◽  
Population Trends ◽  
Science Data

scholarly journals Modeling avian full annual cycle distribution and population trends with citizen science data

2020 ◽  
Vol 30 (3) ◽  
Author(s):  
Daniel Fink ◽  
Tom Auer ◽  
Alison Johnston ◽  
Viviana Ruiz‐Gutierrez ◽  
Wesley M. Hochachka ◽  
...  
Keyword(s):  
Citizen Science ◽  
Annual Cycle ◽  
Population Trends ◽  
Science Data

scholarly journals Model‐based integration of citizen science data from disparate sources increases the precision of bird population trends

2021 ◽  
Author(s):  
Lionel R. Hertzog ◽  
Claudia Frank ◽  
Sebastian Klimek ◽  
Norbert Röder ◽  
Hannah G. S. Böhner ◽  
...  
Keyword(s):  
Citizen Science ◽  
Population Trends ◽  
Science Data ◽  
Bird Population ◽  
Model Based

scholarly journals Integrating structured and unstructured citizen science data to improve wildlife population monitoring

2021 ◽  
Author(s):  
Philipp H Boersch-Supan ◽  
Robert A Robinson
Keyword(s):  
Data Integration ◽  
Citizen Science ◽  
Large Scale ◽  
Population Monitoring ◽  
Analytical Framework ◽  
Good Evidence ◽  
Population Trends ◽  
Data Sources ◽  
Science Data ◽  
Widespread Species

Accurate and robust population trend assessments are key to successful biodiversity conservation. Citizen science surveys have provided good evidence of biodiversity declines whilst engaging people with them. Citizen scientists are also collecting opportunistic biodiversity records at unprecedented scales, vastly outnumbering records gathered through structured surveys. Opportunistic records exhibit spatio-temporal biases and heterogeneity in observer effort and skill, but their quantity offers a rich source of information. Data integration, the combination of multiple information sources in a common analytical framework, can potentially improve inferences about populations compared to analysing either in isolation. We combine count data from a structured citizen science survey and detection-nondetection data from an opportunistic citizen science programme. Population trends were modelled using dynamic N-mixture models to integrate both data sources. We applied this approach to two different inferential challenges arising from sparse data: (i) the estimation of population trends for an area smaller than a structured survey stratum, and (ii) the estimation of national population trends for a rare but widespread species. In both cases, data integration yielded population trajectories similar to those estimated from structured survey data alone but had higher precision when the density of opportunistic records was high. In some cases this allowed inferences about population trends where indices derived from single data sources were too uncertain to assess change. However, there were differences in the trend magnitude between the integrated and the standard survey model. We show that data integration of large-scale structured and unstructured data is feasible and offers potential to improve national and regional wildlife trend estimates, although a need to independently validate trends remains. Smaller gains are achieved in areas where uptake of opportunistic recording is low. The integration of opportunistic records from volunteer-selected locations alone may therefore not adequately address monitoring gaps for management and policy applications. To achieve the latter, scheme organisers should consider providing incentives for achieving representative coverage of target areas in both structured and unstructured recording schemes.

Using opportunistic citizen science data to estimate avian population trends

2018 ◽  
Vol 221 ◽  
pp. 151-159 ◽  
Author(s):  
Joshua J. Horns ◽  
Frederick R. Adler ◽  
Çağan H. Şekercioğlu
Keyword(s):  
Citizen Science ◽  
Population Trends ◽  
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.

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