Citizen Science Data Collection for Integrated Wildlife Population Analyses

Citizen science, or community science, has emerged as a cost-efficient method to collect data for wildlife monitoring. To inform research and conservation, citizen science sampling designs should collect data that match the robust statistical analyses needed to quantify species and population patterns. Further increasing the contributions of citizen science, integrating citizen science data with other datasets and datatypes can improve population estimates and expand the spatiotemporal extent of inference. We demonstrate these points with a citizen science program called iSeeMammals developed in New York state in 2017 to supplement costly systematic spatial capture-recapture sampling by collecting opportunistic data from one-off observations, hikes, and camera traps. iSeeMammals has initially focused on the growing population of American black bear (Ursus americanus), with integrated analysis of iSeeMammals camera trap data with systematic data for a region with a growing bear population. The triumvirate of increased spatial and temporal coverage by at least twofold compared to systematic sampling, an 83% reduction in annual sampling costs, and improved density estimates when integrated with systematic data highlight the benefits of collecting presence-absence data in citizen science programs for estimating population patterns. Additional opportunities will come from applying presence-only data, which are oftentimes more prevalent than presence-absence data, to integrated models. Patterns in data submission and filtering also emphasize the importance of iteratively evaluating patterns in engagement, usability, and accessibility, especially focusing on younger adult and teenage demographics, to improve data quality and quantity. We explore how the development and use of integrated models may be paired with citizen science project design in order to facilitate repeated use of datasets in standalone and integrated analyses for supporting wildlife monitoring and informing conservation.

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Can citizen science analysis of camera trap data be used to study reproduction? Lessons from Snapshot Serengeti program

10.1101/2020.11.30.400804 ◽

2020 ◽

Author(s):

Thel Lucie ◽

Chamaillé-Jammes Simon ◽

Keurinck Léa ◽

Catala Maxime ◽

Packer Craig ◽

...

Keyword(s):

Citizen Science ◽

Camera Trap ◽

Camera Traps ◽

Life History Trait ◽

List Type ◽

Breeding Phenology ◽

Science Data ◽

Morphological Criteria ◽

Wide Range ◽

Trained Observers

AbstractEcologists increasingly rely on camera trap data to estimate a wide range of biological parameters such as occupancy, population abundance or activity patterns. Because of the huge amount of data collected, the assistance of non-scientists is often sought after, but an assessment of the data quality is a prerequisite to their use.We tested whether citizen science data from one of the largest citizen science projects - Snapshot Serengeti - could be used to study breeding phenology, an important life-history trait. In particular, we tested whether the presence of juveniles (less than one or 12 months old) of three ungulate species in the Serengeti: topi Damaliscus jimela, kongoni Alcelaphus buselaphus and Grant’s gazelle Nanger granti could be reliably detected by the “naive” volunteers vs. trained observers. We expected a positive correlation between the proportion of volunteers identifying juveniles and their effective presence within photographs, assessed by the trained observers.We first checked the agreement between the trained observers for age classes and species and found a good agreement between them (Fleiss’ κ > 0.61 for juveniles of less than one and 12 month(s) old), suggesting that morphological criteria can be used successfully to determine age. The relationship between the proportion of volunteers detecting juveniles less than a month old and their actual presence plateaued at 0.45 for Grant’s gazelle and reached 0.70 for topi and 0.56 for kongoni. The same relationships were however much stronger for juveniles younger than 12 months, to the point that their presence was perfectly detected by volunteers for topi and kongoni.Volunteers’ classification allows a rough, moderately accurate, but quick, sorting of photograph sequences with/without juveniles. Obtaining accurate data however appears more difficult. We discuss the limitations of using citizen science camera traps data to study breeding phenology, and the options to improve the detection of juveniles, such as the addition of aging criteria on the online citizen science platforms, or the use of machine learning.

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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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