Lessons from citizen science: Assessing volunteer-collected plant phenology data with Mountain Watch

MacKenzie, Caitlin McDonough; Murray, Georgia; Primack, Richard B.; Weihrauch, Douglas M.

doi:10.1016/j.biocon.2016.07.027

Cited by 61 publications

(38 citation statements)

References 24 publications

(34 reference statements)

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“…Previous studies reported a tendency of volunteers to preferentially report sightings of rarer species (Lepczyk, 2005;Lewandowski & Specht, 2015;McDonough MacKenzie, Murray, Primack, & Weihrauch, 2017). There is also some evidence that false-positive error rates may be higher for rare species (Farmer, Leonard, & Horn, 2012), a phenomenon coined rare-species bias.…”

Section: Discussionmentioning

confidence: 99%

Quantifying data quality in a citizen science monitoring program: False negatives, false positives and occupancy trends

Cruickshank

Bühler²,

Schmidt

2019

Conservat Sci and Prac

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Data collected by volunteers are an important source of information used in species management decisions, yet concerns are often raised over the quality of such data.Two major forms of error exist in occupancy datasets; failing to observe a species when present (imperfect detection-also known as false negatives), and falsely reporting a species as present (false-positive errors). Estimating these rates allows us to quantify volunteer data quality, and may prevent the inference of erroneous trends. We use a new parameterization of a dynamic occupancy model to estimate and adjust for false-negative and false-positive errors, producing accurate estimates of occupancy. We validated this model using simulations and applied it to 12 species datasets collected from a 15-year, large-scale volunteer amphibian monitoring program. False-positive rates were low for most, but not all, species, and accounting for these errors led to quantitative differences in occupancy, although trends remained consistent even when these effects were ignored. We present a model that represents an intuitive way of quantifying the quality of volunteer monitoring datasets, and which can produce unbiased estimates of occupancy despite the presence of multiple types of observation error. Importantly, this allows the quality of volunteer monitoring data to be assessed without relying on comparisons with expert data.

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Section: Discussionmentioning

confidence: 99%

Quantifying data quality in a citizen science monitoring program: False negatives, false positives and occupancy trends

Cruickshank

Bühler²,

Schmidt

2019

Conservat Sci and Prac

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“…For example, Mountain Watch, a ground-based phenology citizen science project, found that initial identifications by volunteers were inconsistent, due to misidentification of species or inaccurate location descriptions. Consequently, the project altered its methods to use permanent plots and better-trained personnel [38]. As it is for any citizen science project, the main ongoing investment is recruiting, retaining, and communicating with volunteers as well as in good data-management practices [39,40].…”

Section: Recommendations For Citizen Science Data Processing Of Remotmentioning

confidence: 99%

Season Spotter: Using Citizen Science to Validate and Scale Plant Phenology from Near-Surface Remote Sensing

et al. 2016

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Abstract:The impact of a rapidly changing climate on the biosphere is an urgent area of research for mitigation policy and management. Plant phenology is a sensitive indicator of climate change and regulates the seasonality of carbon, water, and energy fluxes between the land surface and the climate system, making it an important tool for studying biosphere-atmosphere interactions. To monitor plant phenology at regional and continental scales, automated near-surface cameras are being increasingly used to supplement phenology data derived from satellite imagery and data from ground-based human observers. We used imagery from a network of phenology cameras in a citizen science project called Season Spotter to investigate whether information could be derived from these images beyond standard, color-based vegetation indices. We found that engaging citizen science volunteers resulted in useful science knowledge in three ways: first, volunteers were able to detect some, but not all, reproductive phenology events, connecting landscape-level measures with field-based measures. Second, volunteers successfully demarcated individual trees in landscape imagery, facilitating scaling of vegetation indices from organism to ecosystem. And third, volunteers' data were used to validate phenology transition dates calculated from vegetation indices and to identify potential improvements to existing algorithms to enable better biological interpretation. As a result, the use of citizen science in combination with near-surface remote sensing of phenology can be used to link ground-based phenology observations to satellite sensor data for scaling and validation. Well-designed citizen science projects targeting improved data processing and validation of remote sensing imagery hold promise for providing the data needed to address grand challenges in environmental science and Earth observation.

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“…The predictability of performance based on self-assessment seems to vary for other studies. McDonough et al (2017) found that the self-assessed species identification skills did not correspond to the skills of the citizen scientists. Starr et al (2014) 575 identified a group of citizen scientists who seemed too confident in their abilities, but overall believed that the self-assessment of the majority of their citizen scientists was accurate.…”

Section: Does Participants' Self-assessment Of Confidence Difficultymentioning

confidence: 89%

Training citizen scientists through an online game developed for data quality control

Strobl¹,

Etter²,

Meerveld³

et al. 2019

Preprint

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Some form of training is often necessary in citizen science projects. While in many citizen science projects it is possible to keep tasks simple so that training requirements are minimal, some projects include more challenging tasks and, thus, require more extensive training. Training can hinder joining a project, and therefore most citizen science projects prefer to keep training requirements low. However, training may be needed to ensure good data quality. In this study, we evaluated 10 if an online game that was originally developed for data quality control in a citizen science project, can be used for training for that project. More specifically, we investigated whether the CrowdWater game can be used to train new participants on how to use the virtual staff gauge in the CrowdWater smartphone app for the collection of water level class data. Within this app, the task of placing a virtual staff gauge to start measurements at a new location has proven to be challenging; however this is a crucial task for all subsequent measurements at this location. We analysed the performance of 52 participants in the 15 placement of the virtual staff gauge before and after playing the online CrowdWater game as a form of training. After playing the game, the performance improved for most participants. This suggests that players learned project related tasks intuitively by observing actual gauge placements by other citizen scientists and thus acquired knowledge about how to best use the app instinctively. Interestingly, self-assessment was not a good proxy for the participants' performance or performance increase.These results demonstrate the value of an online game for training, particularly when compared to other information materials, 20 which are often not used extensively by citizen scientist. These findings are useful for the development of training strategies for other citizen science projects because they indicate that gamified approaches might provide valuable alternative training methods.

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Lessons from citizen science: Assessing volunteer-collected plant phenology data with Mountain Watch

Cited by 61 publications

References 24 publications

Quantifying data quality in a citizen science monitoring program: False negatives, false positives and occupancy trends

Quantifying data quality in a citizen science monitoring program: False negatives, false positives and occupancy trends

Season Spotter: Using Citizen Science to Validate and Scale Plant Phenology from Near-Surface Remote Sensing

Training citizen scientists through an online game developed for data quality control

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