Estimating hybridization in the wild using citizen science data: a path forward

2019 ◽

Vol 3 ◽

Author(s):

Congtian Lin ◽

Jiangning Wang ◽

Liqiang Ji

Keyword(s):

Artificial Intelligence ◽

Citizen Science ◽

Higher Plants ◽

Mobile App ◽

Biodiversity Data ◽

Science Data ◽

Biodiversity Research ◽

Large Numbers ◽

In The Wild ◽

Speed Up

Biodiversity research is stepping into a big data era with the rapid increase in the abundance of biodiversity data, especially the large number of species images. It has been a new trend and hot topic on how to utilize artificial intelligence to mine big biodiversity data to support wildlife observation and recognition. In this research, we integrate large numbers of species images, including higher plants, birds and insects, and use a state-of-the-art image deep learning technique to train species auto-recognition models. Currently, we get a model that can recognize more than 900 Chinese birds with top 1 accuracy 81% and top 5 accuracy 95% (top n accuracy means the probability that the correct answer presents in top n predicted results), and more models are coming soon. Based on these models, we developed a platform named Notes of Life (NOL, http://nol.especies.cn), which includes a website and a mobile application (app) for assisting biological scientists and citizen scientists to recognize and record wildlife. Users can upload their observation records and images of wildlife through our mobile app while they are investigating in the wild. The website is used for bulk data uploading and management. Species images can be classified by taxon-specific, plug-in recognition models that speed up the process of identification. There is an expert module in NOL where citizen scientists can work interactively with information provided by biological scientists, and post a species image identification request to experts when they cannot recognize the species by themselves or from models. The expert module is for improving the quality of citizen science data, and it is a supplement of the disadvantage of species auto-recognition models. Above all, NOL embraces the idea that scientific research supports citizen science and citizen science gives feedback to science, and of finding a sustainable way to collect increasingly more reliable data for biodiversity research.

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The Use of Citizen Science to Achieve Multivariate Management Goals on Public Lands

Diversity ◽

10.3390/d13070293 ◽

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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Abundant Citizen Science Data Reveal That the Peacock Butterfly Aglais io Recently Became Bivoltine in Belgium

Insects ◽

10.3390/insects12080683 ◽

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.

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Explaining Spatial Variation in the Recording Effort of Citizen Science Data across Multiple Taxa

PLoS ONE ◽

10.1371/journal.pone.0147796 ◽

2016 ◽

Vol 11 (1) ◽

pp. e0147796 ◽

Cited By ~ 36

Author(s):

Louise Mair ◽

Alejandro Ruete

Keyword(s):

Spatial Variation ◽

Citizen Science ◽

Science Data

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A Conceptual Probabilistic Framework for Annotation Aggregation of Citizen Science Data

Mathematics ◽

10.3390/math9080875 ◽

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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Using citizen science data to monitor the Sustainable Development Goals: a bottom-up analysis

Sustainability Science ◽

10.1007/s11625-021-01001-1 ◽

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.

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