Citizen science data reveals the need for keeping garden plant recommendations up-to-date to help pollinators

AbstractWidespread concern over declines in pollinating insects has led to numerous recommendations of which “pollinator-friendly” plants to grow and help turn urban environments into valuable habitat for such important wildlife. Whilst communicated widely by organisations and readily taken up by gardeners, the provenance, accuracy, specificity and timeliness of such recommendations remain unclear. Here we use data (6429 records) gathered through a UK-wide citizen science programme (BeeWatch) to determine food plant use by the nations’ bumblebee species, and show that much of the plant use recorded does not reflect practitioner recommendations: correlation between the practitioners’ bumblebee-friendly plant list (376 plants compiled from 14 different sources) and BeeWatch records (334 plants) was low (r = 0.57), and only marginally higher than the correlation between BeeWatch records and the practitioners’ pollinator-friendly plant list (465 plants from 9 different sources; r = 0.52). We found pollinator-friendly plant lists to lack independence (correlation between practitioners’ bumblebee-friendly and pollinator-friendly lists: r = 0.75), appropriateness and precision, thus failing to recognise the non-binary nature of food-plant preference (bumblebees used many plants, but only in small quantities, e.g. lavender—the most popular plant in the BeeWatch database—constituted, at most, only 11% of records for any one bumblebee species) and stark differences therein among species and pollinator groups. We call for the provision and use of up-to-date dynamic planting recommendations driven by live (citizen science) data, with the possibility to specify pollinator species or group, to powerfully support transformative personal learning journeys and pollinator-friendly management of garden spaces.

Download Full-text

Comparisons of Citizen Science Data-Gathering Approaches to Evaluate Urban Butterfly Diversity

Insects ◽

10.3390/insects9040186 ◽

2018 ◽

Vol 9 (4) ◽

pp. 186 ◽

Cited By ~ 4

Author(s):

Kathleen Prudic ◽

Jeffrey Oliver ◽

Brian Brown ◽

Elizabeth Long

Keyword(s):

Species Richness ◽

Citizen Science ◽

Relative Effectiveness ◽

Data Gathering ◽

Urban Environments ◽

Butterfly Species ◽

Science Data ◽

Time Period ◽

Effectiveness And Efficiency ◽

Malaise Traps

By 2030, ten percent of earth’s landmass will be occupied by cities. Urban environments can be home to many plants and animals, but surveying and estimating biodiversity in these spaces is complicated by a heterogeneous built environment where access and landscaping are highly variable due to human activity. Citizen science approaches may be the best way to assess urban biodiversity, but little is known about their relative effectiveness and efficiency. Here, we compare three techniques for acquiring data on butterfly (Lepidoptera: Rhopalocera) species richness: trained volunteer Pollard walks, Malaise trapping with expert identification, and crowd-sourced iNaturalist observations. A total of 30 butterfly species were observed; 27 (90%) were recorded by Pollard walk observers, 18 (60%) were found in Malaise traps, and 22 (73%) were reported by iNaturalist observers. Pollard walks reported the highest butterfly species richness, followed by iNaturalist and then Malaise traps during the four-month time period. Pollard walks also had significantly higher species diversity than Malaise traps.

Download Full-text

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.

Download Full-text

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.

Download Full-text

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

Download Full-text

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.

Download Full-text

COVID-19 Pandemic Turns Life-Science Students into “Citizen Scientists”: Data Indicate Multiple Negative Effects of Urbanization on Biota

Sustainability ◽

10.3390/su13052992 ◽

2021 ◽

Vol 13 (5) ◽

pp. 2992

Author(s):

Jens Schirmel

Keyword(s):

Citizen Science ◽

Online Teaching ◽

Teaching Strategy ◽

Ecosystem Functions ◽

Negative Effects ◽

Ecological Data ◽

Pollinating Insects ◽

Impact Of Urbanization ◽

Data Collections ◽

The Impact

The COVID-19 pandemic and its restrictions strongly affect the higher education community and require diverse teaching strategies. We designed a course where we combined online teaching with independently conducted ecological data collections by students using a “citizen science” approach. The aim was to analyze the impact of urbanization on biota by comparing urban and rural grasslands. Seventy-five students successfully conducted the data collections and the results provide evidence for prevailing negative effects of urbanization. Individual numbers of ground-dwelling invertebrates (−25%) and pollinating insects (−33%) were generally lower in urban sites. Moreover, animal and seed predation were reduced in urban grasslands, indicating the potential of urbanization to alter ecosystem functions. Despite the general limitations of online teaching and citizen science approaches, outcomes of this course showed this combination can be a useful teaching strategy, which is why this approach could be used to more actively involve students in scientific research.

Download Full-text

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.

Download Full-text