Header

UZH-Logo

Maintenance Infos

Quantifying data quality in a citizen science monitoring program: False negatives, false positives and occupancy trends


Cruickshank, Sam S; Bühler, C; Schmidt, Benedikt (2019). Quantifying data quality in a citizen science monitoring program: False negatives, false positives and occupancy trends. Conservation Science and Practice, 1(7):e54.

Abstract

Data collected by volunteers are an important source of information used in species management decisions, yet concerns are often raised over the quality of such data. Two major forms of error exist in occupancy datasets; failing to observe a species when present (imperfect detection—also known as false negatives), and falsely reporting a species as present (false‐positive errors). Estimating these rates allows us to quantify volunteer data quality, and may prevent the inference of erroneous trends. We use a new parameterization of a dynamic occupancy model to estimate and adjust for false‐negative and false‐positive errors, producing accurate estimates of occupancy. We validated this model using simulations and applied it to 12 species datasets collected from a 15‐year, large‐scale volunteer amphibian monitoring program. False‐positive rates were low for most, but not all, species, and accounting for these errors led to quantitative differences in occupancy, although trends remained consistent even when these effects were ignored. We present a model that represents an intuitive way of quantifying the quality of volunteer monitoring datasets, and which can produce unbiased estimates of occupancy despite the presence of multiple types of observation error. Importantly, this allows the quality of volunteer monitoring data to be assessed without relying on comparisons with expert data.

Abstract

Data collected by volunteers are an important source of information used in species management decisions, yet concerns are often raised over the quality of such data. Two major forms of error exist in occupancy datasets; failing to observe a species when present (imperfect detection—also known as false negatives), and falsely reporting a species as present (false‐positive errors). Estimating these rates allows us to quantify volunteer data quality, and may prevent the inference of erroneous trends. We use a new parameterization of a dynamic occupancy model to estimate and adjust for false‐negative and false‐positive errors, producing accurate estimates of occupancy. We validated this model using simulations and applied it to 12 species datasets collected from a 15‐year, large‐scale volunteer amphibian monitoring program. False‐positive rates were low for most, but not all, species, and accounting for these errors led to quantitative differences in occupancy, although trends remained consistent even when these effects were ignored. We present a model that represents an intuitive way of quantifying the quality of volunteer monitoring datasets, and which can produce unbiased estimates of occupancy despite the presence of multiple types of observation error. Importantly, this allows the quality of volunteer monitoring data to be assessed without relying on comparisons with expert data.

Statistics

Citations

Dimensions.ai Metrics

Altmetrics

Downloads

7 downloads since deposited on 28 Jun 2019
7 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Evolutionary Biology and Environmental Studies
Dewey Decimal Classification:570 Life sciences; biology
590 Animals (Zoology)
Uncontrolled Keywords:amphibian, monitoring, volunteer, citizen science, occupancy model, false positive, false negative
Language:English
Date:2019
Deposited On:28 Jun 2019 14:38
Last Modified:17 Sep 2019 20:25
Publisher:Wiley-Blackwell Publishing, Inc.
ISSN:2578-4854
OA Status:Green
Free access at:Official URL. An embargo period may apply.
Publisher DOI:https://doi.org/10.1111/csp2.54
Official URL:https://onlinelibrary.wiley.com/doi/10.1111/csp2.54

Download

Download PDF  'Quantifying data quality in a citizen science monitoring program: False negatives, false positives and occupancy trends'.
Preview
Content: Published Version
Filetype: PDF
Size: 5MB
View at publisher
Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0)