How citizen science could improve species distribution models and their independent assessment

Citizen science (CS) currently refers to the participation of non-scientist volunteers in any discipline of conventional scientific research. Over the last two decades, nature-based CS has flourished due to innovative technology, novel devices, and widespread digital platforms used to collect and classify species occurrence data. For scientists, CS offers a low-cost approach of collecting species occurrence information at large spatial scales that otherwise would be prohibitively expensive. We examined the trends and gaps linked to the use of CS as a source of data for species distribution models (SDMs), in order to propose guidelines and highlight solutions. We conducted a quantitative literature review of 207 peer-reviewed articles to measure how the representation of different taxa, regions, and data types have changed in SDM publications since the 2010s. Our review shows that the number of papers using CS for SDMs has increased at approximately double the rate of the overall number of SDM papers. However, disparities in taxonomic and geographic coverage remain in studies using CS. Western Europe and North America were the regions with the most coverage (73%). Papers on birds (49%) and mammals (19.3%) outnumbered other taxa. Among invertebrates, flying insects including Lepidoptera, Odonata and Hymenoptera received the most attention. Discrepancies between research interest and availability of data were as especially important for amphibians, reptiles and fishes. Compared to studies on animal taxa, papers on plants using CS data remain rare. Although the aims and scope of papers are diverse, species conservation remained the central theme of SDM using CS data. We present examples of the use of CS and highlight recommendations to motivate further research, such as combining multiple data sources and promoting local and traditional knowledge. We hope our findings will strengthen citizen-researchers partnerships to better inform SDMs, especially for less-studied taxa and regions. Researchers stand to benefit from the large quantity of data available from CS sources to improve global predictions of species distributions.

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Spatial thinning and class balancing: Key choices lead to variation in the performance of species distribution models with citizen science data

Methods in Ecology and Evolution ◽

10.1111/2041-210x.13525 ◽

2020 ◽

Author(s):

Valerie A. Steen ◽

Morgan W. Tingley ◽

Peter W. C. Paton ◽

Chris S. Elphick

Keyword(s):

Citizen Science ◽

Species Distribution ◽

Species Distribution Models ◽

Distribution Models ◽

Science Data

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Species distribution models for a migratory bird based on citizen science and satellite tracking data

Global Ecology and Conservation ◽

10.1016/j.gecco.2017.08.001 ◽

2017 ◽

Vol 11 ◽

pp. 298-311 ◽

Cited By ~ 21

Author(s):

Christopher L. Coxen ◽

Jennifer K. Frey ◽

Scott A. Carleton ◽

Daniel P. Collins

Keyword(s):

Citizen Science ◽

Species Distribution ◽

Species Distribution Models ◽

Satellite Tracking ◽

Migratory Bird ◽

Tracking Data ◽

Distribution Models

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Combining citizen science species distribution models and stable isotopes reveals migratory connectivity in the secretive Virginia rail

Journal of Applied Ecology ◽

10.1111/1365-2664.12723 ◽

2016 ◽

Vol 54 (2) ◽

pp. 618-627 ◽

Cited By ~ 18

Author(s):

Auriel M. V. Fournier ◽

Alexis R. Sullivan ◽

Joseph K. Bump ◽

Marie Perkins ◽

Mark C. Shieldcastle ◽

...

Keyword(s):

Stable Isotopes ◽

Citizen Science ◽

Species Distribution ◽

Species Distribution Models ◽

Migratory Connectivity ◽

Distribution Models

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Using citizen science monitoring data in species distribution models to inform isotopic assignment of migratory connectivity in wetland birds

10.1101/144527 ◽

2017 ◽

Author(s):

Auriel M. V. Fournier ◽

Kiel L. Drake ◽

Douglas C. Tozer

Keyword(s):

South Carolina ◽

Citizen Science ◽

Species Distribution ◽

Species Distribution Models ◽

Monitoring Program ◽

The United States ◽

Monitoring Data ◽

Migratory Connectivity ◽

Distribution Models ◽

The Pacific

AbstractStable isotopes have been used to estimate migratory connectivity in many species. Estimates are often greatly improved when coupled with species distribution models (SDMs), which temper estimates in relation to occurrence. SDMs can be constructed using from point locality data from a variety of sources including extensive monitoring data typically collected by citizen scientists. However, one potential issue with SDM is that these data oven have sampling bias. To avoid this potential bias, an approach using SDMs based on marsh bird monitoring program data collected by citizen scientists and other participants following protocols specifically designed to maximize detections of species of interest at locations representative of the species range. We then used the SDMs to refine isotopic assignments of breeding areas of autumn-migrating and wintering Sora (Porzana Carolina), Virginia Rails (Rallus limicola), and Yellow Rails (Coturnicops noveboracensis) based on feathers collected from individuals caught at various locations in the United States from Minnesota south to Louisiana and South Carolina. Sora were assigned to an area that included much of the western U.S. and prairie Canada, covering parts of the Pacific, Central, and Mississippi Flyways. Yellow Rails were assigned to a broad area along Hudson and James Bay in northern Manitoba and Ontario, as well as smaller parts of Quebec, Minnesota, Wisconsin, and Michigan, including parts of the Mississippi and Atlantic Flyways. Virginia Rails were from several discrete areas, including parts of Colorado, New Mexico, the central valley of California, and southern Saskatchewan and Manitoba in the Pacific and Central Flyways. Our study demonstrates extensive data from organized citizen science monitoring programs are especially useful for improving isotopic assignments of migratory connectivity in birds, which can ultimately lead to better informed management decisions and conservation actions.

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