Model-based data integration improves species distribution models for data deficient and narrow-ranged hummingbird species

For species with narrow ranges or low population sizes, a deficiency of species occurrence records can limit the capacity to build accurate species distribution models (SDMs). Model-based integration of data from multiple sources has been offered as a solution to improve predictions of species’ distributions at large scales, especially for data-deficient species, but clear empirical demonstrations for this are lacking. The study location was South and Central America. We applied a state-of-the-art data integration technique to model the distributions of 98104 hummingbird species. We fitted SDMs using either presence-absence (PA) data from eBird or presence-only (PO) data from eBird and the Global Biodiversity Information Facility (GBIF) and compared them to integrated SDMs, which utilize both PA and PO data. We fitted generalized linear mixed-effects models and validated them with spatial block cross-validation and expert range map adjusted validation. We also conducted an experiment us..., This data set is a collection of publicly available species and environmental data. They have been used to study populations and distributions of species and their associations with environment. All data processing, analysis and presentations are conducted with R and Rstudio, which are openly available., , This data set is a collection of publicly available species and environmental data. They have been used to study populations and distributions of species and their associations with the environment.

## Description of the data set (Data.7z)

* File 1 Name: Model_objects/INLA_spat_Heliomaster furcifer_fits_thin_not_thin_PA_quad_n_20000_wa_not_offsets_inla.RData
* File 1 Description: Rdata-file contains INLA model objects for three species distribution models, which are fitted with presence-absence data,
presence-only data, or with their combination (PA+PO model). The models are for the Gilded hummingbird (*Heliomaster furcifer*):
* model_fits: model objects of the fitted INLA models
* stk_coll: data stack objects used for fitting the models
* File 2 Name: Prediction_objects/Heliomaster furcifer.csv
* File 2 Description: Csv-file contains spatially explicit model predictions of Blue-tufted starthroat (*Heliomaster furcifer*). Table columns:
* lon: longitude
* lat: ...,