scholarly journals An evaluation of stringent filtering to improve species distribution models from citizen science data

2019 ◽  
Vol 25 (12) ◽  
pp. 1857-1869 ◽  
Author(s):  
Valerie A. Steen ◽  
Chris S. Elphick ◽  
Morgan W. Tingley
Keyword(s):  
Citizen Science ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Distribution Models ◽  
Science Data

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

2020 ◽  
Vol 26 (8) ◽  
pp. 976-986 ◽  
Author(s):  
Orin J. Robinson ◽  
Viviana. Ruiz‐Gutierrez ◽  
Mark D. Reynolds ◽  
Gregory H. Golet ◽  
Matthew Strimas‐Mackey ◽  
...  
Keyword(s):  
Citizen Science ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Spatial Extent ◽  
Distribution Models ◽  
Science Data ◽  
Expert Surveys

Using aerial surveys and citizen science to create species distribution models for an imperiled grouse

2019 ◽  
Vol 29 (3) ◽  
pp. 967-986
Author(s):  
Ashley M. Tanner ◽  
Evan P. Tanner ◽  
Monica Papeş ◽  
Samuel D. Fuhlendorf ◽  
R. Dwayne Elmore ◽  
...  
Keyword(s):  
Citizen Science ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Distribution Models ◽  
Aerial Surveys

scholarly journals Trends and gaps in the use of citizen science derived data as input for species distribution models: A quantitative review

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0234587
Author(s):  
Mariano J. Feldman ◽  
Louis Imbeau ◽  
Philippe Marchand ◽  
Marc J. Mazerolle ◽  
Marcel Darveau ◽  
...  
Keyword(s):  
Citizen Science ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Spatial Scales ◽  
Species Conservation ◽  
Innovative Technology ◽  
Data Types ◽  
Species Occurrence ◽  
Distribution Models ◽  
Digital Platforms

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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