Integrating citizen science data with expert surveys increases accuracy and spatial extent of species distribution models

Orin J Robinson,Daniel Fink,Mark D Reynolds,Matthew Strimas‐Mackey,Gregory H Golet,Viviana Ruiz‐Gutierrez,Luigi Maiorano

doi:10.1111/ddi.13068

Orin J Robinson, Daniel Fink + Show 5 more

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https://doi.org/10.1111/ddi.13068

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Abstract

AbstractAimInformation on species’ habitat associations and distributions, across a wide range of spatial and temporal scales, is a fundamental source of ecological knowledge. However, collecting information at relevant scales is often cost prohibitive, although it is essential for framing the broader context of more focused research and conservation efforts. Citizen science has been signalled as an increasingly important source to fill in data gaps where information is needed to make comprehensive and robust inferences on species distributions. However, there are perceived trade‐offs of combining highly structured, scientific survey data with largely un‐structured, citizen science data.MethodsWe explore these trade‐offs by applying a simplified approach of filtering citizen science data to resemble structured survey data and analyse both sources of data under a common framework. To accomplish this, we integrated high‐resolution survey data on shorebirds in the northern Central Valley of California with observations in eBird for the entire region that were filtered to improve their quality.ResultsThe integration of survey data with the filtered citizen science data resulted in improved inference and increased the extent and accuracy of distribution models on shorebirds for the Central Valley. The structured surveys improved the overall accuracy of ecological inference over models using citizen science data only by increasing the representation of data collected from high‐quality habitats for shorebirds.Main conclusionsThe practical approach we have shown for data integration can also be used to improve the efficiency of designing biological surveys in the context of larger, citizen science monitoring efforts, ultimately reducing the financial and time expenditures typically required of monitoring programs and focused research. The simple method we present can be used to integrate other types of data with more localized efforts, ultimately improving our ecological knowledge on the distribution and habitat associations of species of conservation concern worldwide.

Highlights

Information on species’ habitat associations and distributions are a fundamental source of ecological knowledge (Sofaer et al 2019).This information is often of interest across a broad range of spatial and temporal scales, from high-resolution information that is more relevant for research on habitat selection (Matthiopoulos et al 2011) or needed to inform management objectives (Zipkin et al 2010) to larger-scale inferences that are useful to address broader questions
We have shown that citizen-science data can be filtered to generate a highquality data set that can closely match the resolution and sampling approach of structured surveys, supporting the call for current and future citizen-science projects to collect essential information related to location and effort, as well as complete surveys (Kelling et al, 2019)
The structured surveys were able to improve the ecological inference of the citizen-science data, by improving the representation of sampled habitats that are key for shorebird species

Summary

Introduction

You may have data that is collected at a high spatial resolution using skilled observers, sampling effort is often standardized, and sampling occurs across a habitat gradient that is representative of the region of interest. On the other hand is citizen-science data, which can be collected across a wide range of sampling conditions by observers that vary widely in their level of expertise, collected across a wide range of spatial resolutions, and sampling effort is not standardized. This has created a perceived trade-off between data quality and quantity among data collected from structured, scientific surveys and data collected from larger-scale, volunteer-based monitoring efforts (Figure 1). The logical framework to integrate these two sources of information would be one that would treat them as independent sources of information used to inform a common underlying distribution for a given species (Pacifici et al 2017)

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