TY - JOUR
T1 - Integrating citizen-science and planned-survey data improves species distribution estimates
AU - Zulian, Viviane
AU - Miller, David A.W.
AU - Ferraz, Gonçalo
N1 - Funding Information:
This paper owes a great deal to Reinaldo Guedes who volunteered his free time over the last twelve years to developing and administrating WikiAves, the most successful citizen-science initiative in Brazil. Roost counts were supported in large part by Projeto Charão, in Brazil, Proyecto Selva Pino Paraná, in Argentina, and Guyra Paraguay, in Paraguay. Last, but not least, we are indebted to the thousands of bird observers who uploaded photographs, audiorecordings and birding lists to eBird, WikiAves and Xeno-canto.
Publisher Copyright:
© 2021 The Authors. Diversity and Distributions published by John Wiley & Sons Ltd.
PY - 2021/12
Y1 - 2021/12
N2 - Aim: Mapping species distributions is a crucial but challenging requirement of wildlife management. The frequent need to sample vast expanses of potential habitat increases the cost of planned surveys and rewards accumulation of opportunistic observations. In this paper, we integrate planned-survey data from roost counts with opportunistic samples from eBird, WikiAves and Xeno-canto citizen-science platforms to map the geographic range of the endangered Vinaceous-breasted Parrot. We demonstrate the estimation and mapping of species occurrence based on data integration while accounting for specifics of each dataset, including observation technique and uncertainty about the observations. Location: Argentina, Brazil and Paraguay. Methods: Our analysis illustrates (a) the incorporation of sampling effort, spatial autocorrelation and site covariates in a joint-likelihood, hierarchical, data integration model; (b) the evaluation of the contribution of each dataset, as well as the contribution of effort covariates, spatial autocorrelation and site covariates to the predictive ability of fitted models using a cross-validation approach; and (c) how spatial representation of the latent occupancy state (i.e. realized occupancy) helps identify areas with high uncertainty that should be prioritized in future fieldwork. Results: We estimate a Vinaceous-breasted Parrot geographic range of 434,670 km2, which is three times larger than the “Extant” area previously reported in the IUCN Red List. The exclusion of one dataset at a time from the analyses always resulted in worse predictions by the models of truncated data than by the Full Model, which included all datasets. Likewise, exclusion of spatial autocorrelation, site covariates or sampling effort resulted in worse predictions. Main conclusions: The integration of different datasets into one joint-likelihood model produced a more reliable representation of the species range than any individual dataset taken on its own, improving the use of citizen-science data in combination with planned-survey results.
AB - Aim: Mapping species distributions is a crucial but challenging requirement of wildlife management. The frequent need to sample vast expanses of potential habitat increases the cost of planned surveys and rewards accumulation of opportunistic observations. In this paper, we integrate planned-survey data from roost counts with opportunistic samples from eBird, WikiAves and Xeno-canto citizen-science platforms to map the geographic range of the endangered Vinaceous-breasted Parrot. We demonstrate the estimation and mapping of species occurrence based on data integration while accounting for specifics of each dataset, including observation technique and uncertainty about the observations. Location: Argentina, Brazil and Paraguay. Methods: Our analysis illustrates (a) the incorporation of sampling effort, spatial autocorrelation and site covariates in a joint-likelihood, hierarchical, data integration model; (b) the evaluation of the contribution of each dataset, as well as the contribution of effort covariates, spatial autocorrelation and site covariates to the predictive ability of fitted models using a cross-validation approach; and (c) how spatial representation of the latent occupancy state (i.e. realized occupancy) helps identify areas with high uncertainty that should be prioritized in future fieldwork. Results: We estimate a Vinaceous-breasted Parrot geographic range of 434,670 km2, which is three times larger than the “Extant” area previously reported in the IUCN Red List. The exclusion of one dataset at a time from the analyses always resulted in worse predictions by the models of truncated data than by the Full Model, which included all datasets. Likewise, exclusion of spatial autocorrelation, site covariates or sampling effort resulted in worse predictions. Main conclusions: The integration of different datasets into one joint-likelihood model produced a more reliable representation of the species range than any individual dataset taken on its own, improving the use of citizen-science data in combination with planned-survey results.
UR - http://www.scopus.com/inward/record.url?scp=85115200991&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85115200991&partnerID=8YFLogxK
U2 - 10.1111/ddi.13416
DO - 10.1111/ddi.13416
M3 - Article
AN - SCOPUS:85115200991
VL - 27
SP - 2498
EP - 2509
JO - Diversity and Distributions
JF - Diversity and Distributions
SN - 1366-9516
IS - 12
ER -