Spatial distribution of citizen science casuistic observations for different taxonomic groups

Tiago, Patrícia; Ceia-Hasse, Ana; Marques, Tiago A.; Capinha, César; Pereira, Henrique M.

doi:10.1038/s41598-017-13130-8

Cited by 70 publications

(71 citation statements)

References 25 publications

(29 reference statements)

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“…Moving the laboratory 'into the wild' 38 can overcome the excess of seclusion of controlled laboratory settings of much behavioural research. It is also in line with citizen science principles 39,40 which are increasingly encouraging the opening up of scientific research and practices to the general public [41][42][43][44][45][46][47][48][49] : from the research design, to the data collection, and the interpretation of the results.…”

supporting

confidence: 53%

Gender-based pairings influence cooperative expectations and behaviours

Cigarini

Vicens

Perelló

2020

Sci Rep

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The study explores the expectations and cooperative behaviours of men and women in a lab-in-thefield experiment by means of citizen science practices in the public space. It specifically examines the influence of gender-based pairings on the decisions to cooperate or defect in a framed and discrete Prisoner's Dilemma game after visual contact. Overall, we found that when gender is considered behavioural differences emerge in expectations of cooperation, cooperative behaviours, and their decision time depending on whom the partner is. Men pairs are the ones with the lowest expectations and cooperation rates. After visual contact women infer men's behaviour with the highest accuracy. Also, women take significantly more time to defect than to cooperate, compared to men. Finally, when the interacting partners have the opposite gender they expect significantly more cooperation and they achieve the best collective outcome. Together, the findings suggest that non verbal signals may influence men and women differently, offering novel interpretations to the context-dependence of gender differences in social decision tasks.

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supporting

confidence: 53%

Gender-based pairings influence cooperative expectations and behaviours

Cigarini

Vicens

Perelló

2020

Sci Rep

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“…Papers published in the past few years have identified flaws in citizen-sourced data, including deviations from standard protocols and biases in recording or in the choice of sampling sites 3,4 . Graham Smith, a wildlife ecologist who analyses sightings made by members of the public for the London-based Mammal Society, a British conservation charity, says that Sunday ramblers will ignore yet another rabbit bounding across their path but unfailingly note a more spectacular sighting such as an otter, which is "the most recorded mammal in Britain for its population size".…”

Section: Growing Painsmentioning

confidence: 99%

No PhDs needed: how citizen science is transforming research

Irwin¹

2018

Nature

151

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ilip Meysman knew he had made his mark on Antwerp when he overheard commuters discussing his research project on the train. Then, just a few days later, he saw an advertisement about his work on television. There it was, he says, "in between the toothpaste and George Clooney's Nespresso". As a biogeochemist at the University of Antwerp in Belgium, Meysman wasn't used to drawing so much attention. But that was before he adopted the citizens of northern Belgium as research partners. With the help of the Flemish environmental protection agency and a regional newspaper, Meysman and a team of non-academics attracted more than 50,000 people to CurieuzeNeuzen, an effort to assess the region's air quality (the name is a play on Antwerp dialect for 'nosy' people). The project ultimately distributed air-pollution samplers to 20,000 participants, who took readings for a month (see 'Street science'). More than 99% of the sensors were returned to Meysman's laboratory for analysis, yielding a bounty of 17,800 data points. They provided Meysman and his colleagues with information about nitrogen dioxide concentrations at 'nose height'-a level of the atmosphere that can't be discerned by satellite and would be prohibitively expensive for scientists to measure on their own. "It has given us a data set which it is not possible to get by other means, " says Meysman, who models air quality. Citizen science-active public involvement in scientific research-is growing bigger, more ambitious and more networked. Beyond monitoring pollution and snapping millions of pictures of flora and fauna, people are building Geiger counters to assess radiation levels, photographing stagnant water to help document the spread of mosquito-borne disease, and taking videos of water flow to calibrate flood models. And an increasing number are donating thinking time to help speed up meta-analyses or assess images in ways that algorithms cannot yet match. The movement is surfing wider societal forces, including a thirst for data; the rise of connectedness and low-cost sensor technologies; and a push to improve the transparency and accessibility of science. Increasingly, government institutions and international organizations are getting in on the action. The US and Scottish environmental protection agencies, for example, have incorporated citizen science in their routine work. The United Nations Environment Programme is exploring ways of using citizen science to both monitor the environment and stoke environmental concern. And the European Commission has made a range of funding opportunities available for citizen science within its €80-billion CITIZEN SCIENCE COMES OF AGE Efforts to engage the public in research are bigger and more diverse than ever. But how much more room is there to grow? BY A I S L I N G I R W I N Japanese priest Sadamaru Okano stands beneath a Geiger counter (top left) that sends radiation readings to the Safecast project.

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“…Another possibility involves the mitigation of spatial and temporal bias in the data. These biases are a highly recognised pervasive characteristic of most data sets of biological records (Isaac & Pocock, 2015;Tiago, Ceia-Hasse, Marques, Capinha & Pereira 2017). In this study, we mitigated the impact of spatial bias by down-sampling the number of records in certain regions and avoiding rectification for temporal bias because the records were relatively well distributed through the years.…”

Section: Discussionmentioning

confidence: 99%

A machine learning approach for the spatiotemporal forecasting of ecological phenomena using dates of species occurrence records

Capinha

2018

Preprint

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1. Spatiotemporal forecasts of ecological phenomena are highly useful and significant in scientific and socio-economic applications. Nevertheless, developing the correlative models to make these forecasts is often stalled by the inadequate availability of the ecological timeseries data. On the contrary, considerable amounts of temporally discrete biological records are being stored in public databases, and often include the sites and dates of the observation.While these data are reasonably suitable for the development of spatiotemporal forecast models, this possibility remains mostly untested.2. In this paper, we test an approach to develop spatiotemporal forecasts based on the dates and locations found in species occurrence records. This approach is based on 'timeseries classification', a field of machine learning, and involves the application of a machinelearning algorithm to classify between time-series representing the environmental conditions that precede the occurrence records and time-series representing other environmental conditions, such as those that generally occur in the sites of the records. We employed this framework to predict the timing of emergence of fruiting bodies of two mushroom species (Boletus edulis and Macrolepiota procera) in countries of Europe, from 2009 to 2015. We compared the predictions from this approach with those from a 'null' model, based on the calendar dates of the records.3. Forecasts made from the environmental-based approach were consistently superior to those drawn from the date-based approach, averaging an area under the receiver operating characteristic curve (AUC) of 0.9 for B. edulis and 0.88 for M. procera, compared to an average AUC of 0.83 achieved by the null models for both species. Prediction errors were distributed across the study area and along the years, lending support to the spatiotemporal representativeness of the values of accuracy measured. 4. Our approach, based on species occurrence records, was able to provide useful forecasts of the timing of emergence of two mushroom species across Europe. Given the increased availability and information contained in this type of records, particularly those supplemented with photographs, the range of events that could be possible to forecast is vast.

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Spatial distribution of citizen science casuistic observations for different taxonomic groups

Cited by 70 publications

References 25 publications

Gender-based pairings influence cooperative expectations and behaviours

Gender-based pairings influence cooperative expectations and behaviours

No PhDs needed: how citizen science is transforming research

A machine learning approach for the spatiotemporal forecasting of ecological phenomena using dates of species occurrence records

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