Integrating citizen-science and planned-survey data improves species distribution estimates

• Autor(a) principal: Zulian, Viviane
• Data de Publicação: 2021
• Outros Autores: Miller, David A.W., Oliveira, Gonçalo Nuno Côrte-Real Ferraz de
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UFRGS
• Texto Completo: http://hdl.handle.net/10183/274358
• topic: Ciência cidadã; Modelos de distribuição de espécies; Papagaio-de-peito-roxo Amazona vinacea; Data integration models; Endangered species; Geographic range

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.