Statistics for citizen science: extracting signals of change from noisy ecological data

By Nick J B Isaac, A. J. Strien, Tom A. August, M. Zeeuw, David Roy. 2014

516 citations recorded by the source corpus.

Policy‐makers increasingly demand robust measures of biodiversity change over short time periods. Long‐term monitoring schemes provide high‐quality data, often on an annual basis, but are taxonomically and geographically restricted. By contrast, opportunistic biological records are relatively unstructured but vast in quantity. Recently, these data have been applied to increasingly elaborate science and policy questions, using a range of methods. At present, we lack a firm understanding of which methods, if any, are capable of delivering unbiased trend estimates on policy‐relevant time‐scales. We identified a set of candidate methods that employ data filtering criteria and/or correction factors to deal with variation in recorder activity. We designed a computer simulation to compare the statistical properties of these methods under a suite of realistic data collection scenarios. We measured the Type I error rates of each method–scenario combination, as well as the power to detect genuine trends. We found that simple methods produce biased trend estimates, and/or had low power. Most methods are robust to variation in sampling effort, but biases in spatial coverage, sampling effort per visit, and detectability, as well as turnover in community composition, all induced some methods to fail. No method was wholly unaffected by all forms of variation in recorder activity, although some performed well enough to be useful. We warn against the use of simple methods. Sophisticated methods that model the data collection process offer the greatest potential to estimate timely trends, notably Frescalo and occupancy–detection models. The potential of these methods and the value of opportunistic data would be further enhanced by assessing the validity of model assumptions and by capturing small amounts of information about sampling intensity at the point of data collection.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/54d9fc3ed4937ee546ed45aee7bef16b4ae3775d

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Citations in the corpus

Air pollution and its effects on lichens, bryophytes, and lichen‐feeding Lepidoptera: review and evidence from biological records (2015, methodology): “…a long tradition of inference from maps (Preston, 2013), with, older, largely descriptive approaches to environmental change being supplemented over the past 20 years with new methods designed to model potential biases in biological records datasets (Prendergast et al., 1993; Isaac et al., 2014).”
Geographical range margins of many taxonomic groups continue to shift polewards (2015, unknown): “Variation in levels of recorder effort across spatial and temporal scales can present problems to ecologists aiming to quantify range shifts (Tingley & Beissinger, 2009; Isaac et al., 2014).”
The use of opportunistic data for IUCN Red List assessments (2015, background): “Isaac et al. (2014) tested a number of approaches for estimating trends from noisy data.”
The Biological Records Centre: a pioneer of citizen science (2015, background): “…certain is known about the intervening gaps in space and time, or the completeness of the records, so consequently there are challenges with the interpretation and analysis of these data, including geographic or spatial bias (Hill, 2012; Van Strien, van Swaay & Termaat, 2013; Isaac et al., 2014).”
Bias and information in biological records (2015, background): “The extent of this variation in space and time is therefore a critical issue for the robustness of scientific inferences from biological records (Isaac et al., 2014).”
Using opportunistic citizen science data to estimate avian population trends (2018, background): “Differences in visitation rates and topography at finer spatial scales may confound these results, though some of this geographic variance, like differences in sampling intensity, may be controlled for through the use of LLA (Isaac et al., 2014).”
Analytical guidelines to increase the value of community science data: An example using eBird data to estimate species distributions (2021, result): “Including information on the observation process has generally been shown to produce more accurate and robust results (Johnston et al., 2018; Isaac et al., 2014).”
Emerging opportunities and challenges for passive acoustics in ecological assessment and monitoring (2018, background): “…accessible and less computationally expensive Bayesian inference methods for complex hierarchical and occupancy models is increasingly enabling multiple sources of uncertainty to be incorporated into spatiotemporal models (e.g., Isaac et al., 2014; Ruiz- Gutierrez, Hooten, & Campbell Grant, 2016).”
Unstructured citizen science data fail to detect long‐term population declines of common birds in Denmark (2016, methodology): “…species lists matched the trends of a robust monitoring scheme better than presence-only data (van Strien et al., 2010), and strong trends in structured monitoring data may be recovered from unstructured data when analysed with occupancy models (van Strien et al., 2013; Isaac et al., 2014).”
Data Integration for Large-Scale Models of Species Distributions. (2020, background): “data alone is attractive, the potential for bias means we may be on thin ice regarding the validity of predictions obtained from them [75,76].”