scholarly journals Accounting for spatial varying sampling effort due to accessibility in Citizen Science data: A case study of moose in Norway

2020 ◽  
pp. 100446 ◽  
Author(s):  
Jorge Sicacha-Parada ◽  
Ingelin Steinsland ◽  
Benjamin Cretois ◽  
Jan Borgelt
Keyword(s):  
Citizen Science ◽  
Sampling Effort ◽  
Science Data

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 Citizen science with colour blindness: A case study on the Forel-Ule scale

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249755
Author(s):  
Olivier Burggraaff ◽  
Sanjana Panchagnula ◽  
Frans Snik
Keyword(s):  
Citizen Science ◽  
Data Entry ◽  
Science Data ◽  
Training Materials ◽  
Colour Scale ◽  
The Social ◽  
Uncertainty Estimates ◽  
Colour Blindness ◽  
The Impact

Many citizen science projects depend on colour vision. Examples include classification of soil or water types and biological monitoring. However, up to 1 in 11 participants are colour blind. We simulate the impact of various forms of colour blindness on measurements with the Forel-Ule scale, which is used to measure water colour by eye with a 21-colour scale. Colour blindness decreases the median discriminability between Forel-Ule colours by up to 33% and makes several colour pairs essentially indistinguishable. This reduces the precision and accuracy of citizen science data and the motivation of participants. These issues can be addressed by including uncertainty estimates in data entry forms and discussing colour blindness in training materials. These conclusions and recommendations apply to colour-based citizen science in general, including other classification and monitoring activities. Being inclusive of the colour blind increases both the social and scientific impact of citizen science.

Collaborative approaches to accessing and utilising historical citizen science data: a case-study with spearfishers from eastern Australia

2015 ◽  
Vol 66 (3) ◽  
pp. 195 ◽  
Author(s):  
Daniel C. Gledhill ◽  
Alistair J. Hobday ◽  
David J. Welch ◽  
Stephen G. Sutton ◽  
Matthew J. Lansdell ◽  
...  
Keyword(s):  
Environmental Change ◽  
Citizen Science ◽  
Marine Species ◽  
Scientific Data ◽  
Science Data ◽  
Coastal Fish ◽  
Eastern Australia ◽  
Fit For Purpose ◽  
The 1960S

Scientists are increasingly utilising non-traditional data to assist with defining biological baselines and for monitoring environmental change. These data present challenges not encountered with traditional, fit-for-purpose scientific data, including engaging with data owners, building trust and maintaining relationships, analysing and interpreting data collected under varying methodologies, and the possibility that data may not suit an intended purpose. Here we describe engagement activities undertaken with recreational spearfishers to collate and examine spearfishing club data collected from competitions held throughout south-eastern Australia from the 1960s until the present, representing one of the most extensive citizen science datasets for marine species in the region. The data proved suitable for demonstrating change in coastal fish communities, some of which were consistent with expectations given a warming climate over the period considered. With an attitudinal survey of divers we also asked about their experience of environmental change, and interaction with management over recent decades. Mutually beneficial outcomes include: collating and archiving significant data that may otherwise have been lost; improved understanding of spearfisher concerns and experiences; improved engagement between collaborators; and recognition of spearfishers’ desire for better engagement in science and management. Lessons learnt may be broadly applicable to improving collaboration between recreational fishers, citizen science groups, researchers and managers.

Predicting the likely impact of urbanisation on bat populations using citizen science data, a case study for Norfolk, UK

2017 ◽  
Vol 162 ◽  
pp. 44-55 ◽  
Author(s):  
Jennifer A. Border ◽  
Stuart E. Newson ◽  
David C.J. White ◽  
Simon Gillings
Keyword(s):  
Citizen Science ◽  
Science Data

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 Modelling the Heterogeneity within Citizen Science Data for Biodiversity Research

2021 ◽  
Vol 5 ◽  
Author(s):  
Diana Bowler ◽  
Nick Isaac ◽  
Aletta Bonn
Keyword(s):  
Citizen Science ◽  
Heterogeneous Data ◽  
Sampling Effort ◽  
Occupancy Models ◽  
Species Occurrence ◽  
Science Data ◽  
Global Biodiversity Information Facility ◽  
Occurrence Data ◽  
Occupancy Detection ◽  
Species Population

Large amounts of species occurrence data are compiled by platforms such as the Global Biodiversity Information Facility (GBIF) but these data are collected by a diversity of methods and people. Statistical tools, such as occupancy-detection models, have been developed and tested as a way to analyze these heterogeneous data and extract information on species’ population trends. However, these models make many assumptions that might not always be met. More detailed metadata associated with occurrence records would help better describe the observation/detection submodel within occupancy models and improve the accuracy/precision of species’ trend estimates. Here, we present examples of occupancy-detection models applied to citizen science datasets, including dragonfly data in Germany, and typical approaches to account for variation in sampling effort and species detectability, including visit covariates, such as list length. Using results from a recent questionnaire in Germany asking citizen scientists about why and how they collect species occurrence data, we also characterize the different approaches that citizen scientists take to sample and report species observations. We use our findings to highlight examples of key metadata that are often missing (e.g., length of time spent searching, complete checklist or not) in data sharing platforms but would greatly aid modelling attempts of heterogeneous species occurrence data.

scholarly journals Assessing citizen science data quality: an invasive species case study

2011 ◽  
Vol 4 (6) ◽  
pp. 433-442 ◽  
Author(s):  
Alycia W. Crall ◽  
Gregory J. Newman ◽  
Thomas J. Stohlgren ◽  
Kirstin A. Holfelder ◽  
Jim Graham ◽  
...  
Keyword(s):  
Invasive Species ◽  
Data Quality ◽  
Citizen Science ◽  
Science Data

scholarly journals Utility of citizen science data: A case study in land-based shark fishing

PLoS ONE ◽  
2019 ◽  
Vol 14 (12) ◽  
pp. e0226782 ◽  
Author(s):  
Kesley J. Gibson ◽  
Matthew K. Streich ◽  
Tara S. Topping ◽  
Gregory W. Stunz
Keyword(s):  
Citizen Science ◽  
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.

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