DOI: 10.5061/dryad.vq83bk3r5
title: Host diversity outperforms climate as a global driver of symbiont diversity in the bird-feather mite system

description: 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.
description: 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<i>.</i> (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<sup>®</sup>) 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.