Distribution models using semi-structured community science data outperform unstructured-data models for a data-poor species, the Plain Tyrannulet

GORLERI FC; HOCHACHKA W; ARETA JI

THE CONDOR

COOPER ORNITHOLOGICAL SOC

Año: 2021 vol. 123 p. 1 - 17

0010-5422

Modeling the distribution of a data-poor species is challenging due to a reliance on unstructured data that often lacksrelevant information on sampling and produces coarse-resolution outputs of varying accuracy. Data on sampling effortassociated with higher-quality, semi-structured data derived from some community science programs can be used toproduce more precise models of distribution, albeit at a cost of using fewer data. Here, we used semi-structured datato model the seasonal ranges of the Plain Tyrannulet (Inezia inornata), a poorly known Austral?Neotropical migrant,and compared predictive performance to models built with the full unstructured dataset of the species. By comparingthese models, we examined the relatively unexplored tradeoff between data quality and data quantity for modeling ofa data-sparse species. We found that models using semi-structured data outperformed unstructured-data models inthe predictive accuracy metrics (mean squared error, area under the curve, kappa, sensitivity, and specificity), despiteusing only 30% of the available detection records. Moreover, semi-structured models were more biologically accurate,indicating that the tyrannulet favors arboreal habitats in dry and hot lowlands during the breeding season (Chaco re-gion) and is associated with proximity to rivers in tropical and wet areas during the nonbreeding season (Pantanal, Beni,and southwest Amazonia). We demonstrate that more detailed insights into distributional patterns can be gained fromeven small quantities of data when the data are analyzed appropriately. The use of semi-structured data promises to beof wide applicability even for data-poor bird species, helping refine information on distribution and habitat use, neededfor effective assessments of conservation status.