Aim: The simultaneous influence of abiotic and biotic factors as main drivers of global species distributions remains poorly understood, especially in host-dependent groups. In this study, we diverge from traditional macroecological approaches by considering both biotic (avian species diversity) and abiotic (climatic) factors in determining the global distribution pattern of feather mite species richness, one of the most abundant and diverse bird ectosymbionts.

Location: Global.

Methods: We used a global dataset of feather mite-bird interactions published in 2016, complemented with an up-to-date literature survey. We created statistical models designed to explain the effect of abiotic (i.e., temperature, precipitation, and energy-related variables) and biotic factors (bird species richness) on the species richness of feather mites. We used these models to predict global distribution patterns of mites and estimate each explanatory variable’s relative importance in temperate and tropical regions.

Results: According to our models, bird species richness accounts for ~63% of the global distribution pattern of mites, which is ten times more relevant than climatic variables. Among abiotic drivers, precipitation intensity and seasonality were the most important variables, accounting for 10% of mite species richness. This figure is lower in tropical regions, where biotic factors are seven times more important than in temperate regions.

Main Conclusions: We demonstrate that global mite diversity was primarily determined by biotic and, to a lesser extent, abiotic factors. The relative importance of the predictive variables, however, varied between tropical and temperate regions. The strong association between bird species richness and feather mite species diversity at a global scale raises concerns about the potential for future co-extinctions.

We used a published global data set containing records of feather mites (hereafter referred to as mites) interactions in 147 countries (Doña et al., 2016). This dataset includes 12,036 records published between 1882 and 2015 that document the association of 1,887 species of mites to 2,234 species of birds. To minimize contamination, identification issues, and sampling mistakes, we only used records that: (i) included well established mite-bird associations and were considered of high quality by Doña et al. (2016); and (ii) studies that presented sampling effort (number of sampled birds and their associated feather mites) and location (geographical coordinates).

Because this dataset was published in 2016, we complemented it by carrying out an additional search in two bibliographic databases (Scopus and Web of Science^®) with articles published in 2016 and 2017. For this new search, we used the following keywords: “feather mite” AND “bird”, “mite” AND “bird”, “ectoparasite” AND “bird” (Appendix S1). We also filtered these results by only selecting papers that followed the criteria mentioned above. The expanded database’s localities include both polar circles (68ºS to 78ºN, covering ~ 16,750 km of latitude) and four continents (179ºW to 179ºE, covering ~18,060 km of longitude).

The historical mean of climatic and productivity variables was obtained from WordClim v. 2.0 (Fick & Hijmans, 2017) and ENVIREM (Title & Bemmels, 2018) for each site in our dataset based on latitude and longitude. In addition, we defined grids with a spatial resolution of 0.5º (~ 55km in the equatorial region) to obtain the global climatic data from the grid centroid. Climatic data were categorized into three groups: temperature, precipitation, and energy (see Tab. S1 in Supporting Information). Because the data set from WordClim and ENVIREM have different raster resolutions, we adjusted them to the same spatial resolution (i.e., 0.5º), and used it as the analytical unit in the predictive models (see below). Therefore, our final data set comprises two different scales: (1) site-scale, points encompasses sampled birds and their associated feather mites; and (2) global-scale, grids with a resolution of 0.5º covering every terrestrial ecosystem where bird species richness and climatic data were obtained for predicting global patterns of feather mites based on models produced at the site-scale. At the global scale, climatic data was obtained from each cell in 25.200 cells. Global avian richness was extracted from BirdLife International (http://www.birdlife.org) using the sum of overlapped shapefiles of all species within each 0.5º grid, which generates a global bird richness map (Supporting Information Fig. S1). The climatic data were used as predictors of the abiotic model, while bird richness as the predictor of the biotic model.