Dataset Information

Differential reporting of biodiversity in two citizen science platforms during COVID-19 lockdown in Colombia.

ABSTRACT: The COVID-19 pandemic highlighted the potential of using data from long-term citizen science projects to answer questions about the impacts of unexpected events on biodiversity. We evaluate the suitability of data from the citizen science platforms iNaturalist and eBird to describe the effects of the "anthropause" on biodiversity observation in Colombia. We compared record distribution according to human footprint, sampling behaviors, overall and conservation priority species composition during the strictest phase of the COVID-19 lockdown in 2020 to the same periods in 2015-2019. Overall participation in both platforms during the lockdown was high when compared to previous years, but records were concentrated on highly-transformed regions, had lower sampling efforts, and fewer species were recorded. For eBird, species composition was similar to that observed in previous years, and records of species of conservation concern declined in proportion to the decrease in overall species richness across samples. For iNaturalist, the species pool sampled each year remained too dissimilar for comparisons. Once differences in observer behaviors are accounted for, data from these platforms can be used in unplanned comparisons of relatively common species, in regions with high levels of human transformation, and at narrowly defined geographical contexts. To increase the potential of citizen science to monitor rarer species, more natural areas, or be used in large-scale analyses, we need to build and strengthen more diverse networks of observers that can further promote decentralization, democratization, and cost-effectiveness in biodiversity research.

SUBMITTER: Sanchez-Clavijo LM

PROVIDER: S-EPMC9186113 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Differential reporting of biodiversity in two citizen science platforms during COVID-19 lockdown in Colombia.

Sánchez-Clavijo Lina María LM Martínez-Callejas Sindy Jineth SJ Acevedo-Charry Orlando O Diaz-Pulido Angélica A Gómez-Valencia Bibiana B Ocampo-Peñuela Natalia N Ocampo David D Olaya-Rodríguez María Helena MH Rey-Velasco Juan Carlos JC Soto-Vargas Carolina C Ochoa-Quintero Jose Manuel JM

Biological conservation 20210318

The COVID-19 pandemic highlighted the potential of using data from long-term citizen science projects to answer questions about the impacts of unexpected events on biodiversity. We evaluate the suitability of data from the citizen science platforms iNaturalist and eBird to describe the effects of the "anthropause" on biodiversity observation in Colombia. We compared record distribution according to human footprint, sampling behaviors, overall and conservation priority species composition during ...[more]

PMID: 35702146

Similar Datasets

Project description:Tracking the state of biodiversity over time is critical to successful conservation, but conventional monitoring schemes tend to be insufficient to adequately quantify how species' abundances and distributions are changing. One solution to this issue is to leverage data generated by citizen scientists, who collect vast quantities of data at temporal and spatial scales that cannot be matched by most traditional monitoring methods. However, the quality of citizen science data can vary greatly. In this paper, we develop three metrics (inventory completeness, range completeness, spatial bias) to assess the adequacy of spatial observation data. We explore the adequacy of citizen science data at the species level for Australia's terrestrial native birds and then model these metrics against a suite of seven species traits (threat status, taxonomic uniqueness, body mass, average count, range size, species density, and human population density) to identify predictors of data adequacy. We find that citizen science data adequacy for Australian birds is increasing across two of our metrics (inventory completeness and range completeness), but not spatial bias, which has worsened over time. Relationships between the three metrics and seven traits we modelled were variable, with only two traits having consistently significant relationships across the three metrics. Our results suggest that although citizen science data adequacy has generally increased over time, there are still gaps in the spatial adequacy of citizen science for monitoring many Australian birds. Despite these gaps, citizen science can play an important role in biodiversity monitoring by providing valuable baseline data that may be supplemented by information collected through other methods. We believe the metrics presented here constitute an easily applied approach to assessing the utility of citizen science datasets for biodiversity analyses, allowing researchers to identify and prioritise regions or species with lower data adequacy that will benefit most from targeted monitoring efforts.

Project description:Color research continuously demands better methods and larger sample sizes. Citizen science (CS) projects are producing an ever-growing geo- and time-referenced set of photographs of organisms. These datasets have the potential to make a huge contribution to color research, but the reliability of these data need to be tested before widespread implementation.We compared the difference between color extracted from CS photographs with that of color extracted from controlled lighting conditions (i.e., the current gold standard in spectrometry) for both birds and plants. First, we tested the ability of CS photographs to quantify interspecific variability by assessing > 9,000 CS photographs of 537 Australian bird species with controlled museum spectrometry data. Second, we tested the ability of CS photographs to quantify intraspecific variability by measuring petal color data for two plant species using seven methods/sources with varying levels of control.For interspecific questions, we found that by averaging out variability through a large sample size, CS photographs capture a large proportion of across species variation in plumage color within the visual part of the spectrum (R2 = 0.68-0.71 for RGB space and 0.72-0.77 for CIE-LAB space). Between 12 and 14 photographs per species are necessary to achieve this averaging effect for interspecific studies. Unsurprisingly, the CS photographs taken with commercial cameras failed to capture information in the UV part of the spectrum. For intraspecific questions, decreasing levels of control increase the color variation but averaging larger sample sizes can partially mitigate this, aside from particular issues related to saturation and irregularities in light capture.CS photographs offer a very large sample size across space and time which offers statistical power for many color research questions. This study shows that CS photographs contain data that lines up closely with controlled measurements within the visual spectrum if the sample size is large enough, highlighting the potential of CS photographs for both interspecific and intraspecific ecological or biological questions. With regard to analyzing color in CS photographs, we suggest, as a starting point, to measure multiple random points within the ROI of each photograph for both patterned and unpatterned patches and approach the recommended sample size of 12-14 photographs per species for interspecific studies. Overall, this study provides groundwork in analyzing the reliability of a novel method, which can propel the field of studying color forward.

Project description:The coronavirus disease of 2019 (COVID-19) pandemic has impacted educational systems worldwide during 2020, including primary and secondary schooling. To enable students of a local secondary school in Brisbane, Queensland, to continue with their practical agricultural science learning and facilitate online learning, a "Grass Gazers" citizen science scoping project was designed and rapidly implemented as a collaboration between the school and a multidisciplinary university research group focused on pollen allergy. Here, we reflect on the process of developing and implementing this project from the perspective of the school and the university. A learning package including modules on pollen identification, tracking grass species, measuring field greenness, using a citizen science data entry platform, forensic palynology, as well as video guides, risk assessment and feedback forms were generated. Junior agriculture science students participated in the learning via online lessons and independent data collection in their own local neighborhood and/or school grounds situated within urban environments. The university research group and school coordinator, operating in their own distributed work environments, had to develop, source, adopt, and/or adapt material rapidly to meet the unique requirements of the project. The experience allowed two-way knowledge exchange between the secondary and tertiary education sectors. Participating students were introduced to real-world research and were able to engage in outdoor learning during a time when online, indoor, desk-based learning dominated their studies. The unique context of restrictions imposed by the social isolation policies, as well as government Public Health and Department of Education directives, allowed the team to respond by adapting teaching and research activity to develop and trial learning modules and citizen science tools. The project provided a focus to motivate and connect teachers, academic staff, and school students during a difficult circumstance. Extension of this citizen project for the purposes of research and secondary school learning has the potential to offer ongoing benefits for grassland ecology data acquisition and student exposure to real-world science.

Dataset Information

Differential reporting of biodiversity in two citizen science platforms during COVID-19 lockdown in Colombia.

Publications

Differential reporting of biodiversity in two citizen science platforms during COVID-19 lockdown in Colombia.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets