DOI: 10.6084/m9.figshare.17491460.v1
title: Pigeault et al Raw Data - Determinants of haemosporidian single- and co-infection risks in western Palearctic birds.xlsx

description: <b>Determinants of haemosporidian single- and co-infection risks in western Palearctic birds</b> Co-infections with multiple pathogens are common in the wild and may act as a strong selective pressure on both host and parasite evolution. Yet, contrary to single infection, the factors that shape co-infection risk are largely under-investigated. Here, we explored the extent to which bird ecology and phylogeny impact single- and co-infection probabilities by haemosporidian parasites using large datasets from museum collections and a Bayesian phylogenetic modelling framework. While both phylogeny and species attributes (e.g. size of the geographic range, life-history strategy, migration) were relevant predictors of co-infection risk, these factors were less pertinent in predicting the probability of being single infected. Our study suggests that co-infection risk is under a stronger deterministic control than single-infection risk. These results underscore the combined influence of host evolutionary history and species attributes in determining single- and co-infection pattern providing new avenues regarding our ability to predict infection risk in the wild. <b>1- <i>Avian samples and parasites detection: </i></b>Our data set includes 1361 samples of 151 species encompassing 44 families and 18 orders (<b>see Appendix 1</b>, <b>Table S1</b>). Sampling was conducted on salvaged birds that were obtained between 1990 and 2019. and consisted of tissues (muscle and liver) stored in 85% ethanol at 4°C at the Cantonal Museum of Zoology in Lausanne (855 specimens) and in 90% ethanol at -20°C at the Natural History Museum of Geneva (506 specimens). For each individual, parasites (i.e. <i>Haemoproteus, Leucocytozoon </i>and<i> Plasmodium</i>) were detected from tissue samples using molecular methods. Specifically, a nested PCR (Hellgren <i>et al.</i> 2004, <i>J. Parasitol.</i>, 90, 797–802) was performed in triplicates on all samples after DNA was extracted from tissues using a DNeasy Blood & Tissue Kit (Qiagen, Switzerland) following the manufacturer's instructions. Nested PCR products were visualized on agarose gels after electrophoresis to identify infected samples. This nested PCR protocol does not allow to detect co-infections between parasites of the genera <i>Haemoproteus</i> and <i>Plasmodium</i>. Therefore, all positive samples were sequenced in both directions as in van Rooyen <i>et al.</i> (2013, <i>Malar. J.</i>, 12, 40) and identified by performing a local BLAST search in the MalAvi database (Bensch <i>et al.</i> 2009, <i>Mol. Ecol. Resour.</i>, 9, 1353–1358). Co-infections by <i>Haemoproteus</i> and <i>Plasmodium</i> were identified by analyzing double nucleotide peaks on sequence chromatographs. We re-amplified and re-sequenced all the samples for which the chromatograph could not reliably identify the parasite sequences. All sequences were edited using Geneious v8.0.5. Birds not infected by any parasites were classified as “not infected”, birds infected with a single parasite genus were classified as “single infected” and those infected with at least two different genera as “co-infected” (Pigeault <i>et al.</i> 2018, <i>Int. J. Parasitol.</i>, 48, 1079–1087).<b><i>2- Life-history strategies, ecological and behavioral characteristics </i></b>We used published trait data to position each species along the slow-fast continuum of life-history variation (Storchová & Hořák 2018, <i>Glob. Ecol. Biogeogr.</i>, 27, 400–406). More specifically, bird position was represented by the first axis of a principal component analysis (PCA) performed on nine variables describing bird reproductive traits (clutch size, number of broods per year, average length, width and weight of the egg, incubation period, fledging age, age at first breeding) and maximum lifespan (<b>Appendix 1, Table S1</b>). The first axis (Dim.1_Slow_Fast) explained 62.7% of the variability and represented a gradient going from fast (negative values) to slow (positive values) life-history strategies.The trophic niche of bird species was estimated using 35 variables describing the diet during the breeding season (Pearman <i>et al.</i> 2014, <i>Glob. Ecol. Biogeogr.</i>, 23, 414–424). Specifically, we considered 14 variables characterizing diet, nine variables characterizing food acquisition behavior, nine variables characterizing the substrate from which food is taken (<i>e.g.</i> air, water surface, mud, canopy) and three variables characterizing the daily foraging period (<b>Appendix 1</b>, <b>Table S2</b>). As in Pearman <i>et al.</i> (2014), we also included body weight as a surrogate for total energy requirements. These variables were scored as either 0 or 1, with the exception of body weight, which was scored as the average weight of individuals during the breeding season (Pearman <i>et al.</i> 2014,<i> Glob. Ecol. Biogeogr.</i>, 23, 414–424). Trophic niches were represented by the scores of each species along the first two axes of a Hill-Smith ordination (denoted OA; Hill & Smith 1976, <i>Taxon</i>, 25, 249–255). These axes roughly corresponded to the structure (from open to forest habitats; OA1 = 19.3%) and the height (from underwater and ground to foraging in trees or in flight; OA2 = 12.6%) of the foraging environment.The remaining traits (nest type and migration status) were extracted from Storchová & Hořák (2018). Nest type was categorized as either “open” or “closed” while migration status was categorized as “sedentary” (species living in the same area in both the breeding and the non-breeding season), “migratory” (species migrates between breeding and non-breeding season) and “facultative migrant” (species makes irregular shifts in breeding and/or nonbreeding season).<b><i>3- Climatic niche breadth, climatic niche position and geographic range size. </i></b>Estimating species climatic realized niches (i.e. the set of suitable environmental conditions accessible to the species and constrained by biotic interactions; Jackson and Overpeck, 2000, <i>pbio</i>, 26, 194–220) requires data for the full geographical range of species together with the corresponding environmental variables (Guisan <i>et al.</i>, 2017, Cambridge University Press). For each species, we estimated its climatic niche by cross-referencing IUCN range maps with climatic data. Climatic layers for 19 bioclimatic variables were extracted from worldclim at a 10’ resolution (roughly 340 km² at the equator). From these variables, we performed a PCA from which we extracted the two first axes, which explained 55% and 19% of the total variance, respectively. To obtain the climatic niche of each species, we first projected the environmental values corresponding to the geographic range coordinates falling inside the IUCN polygon within the two-dimensional space defined by the two PCA axes. We then used a kernel density estimator (KDE) to delineate species envelopes. KDE have proved useful to characterize complex and potentially irregular shapes (Blonder <i>et al.</i> 2014, <i>Glob. Ecol. Biogeogr.</i>, 23, 595–609) and are increasingly used to characterize climatic niches (e.g. Broennimann <i>et al.</i> 2012, <i>Glob. Ecol. Biogeogr.</i>, 21, 481–497). For each species, the bandwidth of the KDE was estimated from the data using a Hpi multivariate generalization of the plug-in bandwidth selector (Wand & Jones 1994, CRC Press, London). Envelopes were then defined as the minimum threshold of probability density that included 99% of points (to leave out environmentally atypical occurrences). From species envelopes, we extracted its area (niche breadth) and computed its centroid as the mean of point coordinates falling inside the delimited niche. We then extracted the coordinates of the centroid on each of the two PCA axes. To test the robustness of our results we used two other algorithms to delineate species realized niches: convex hulls and alpha hulls. The area of the geographic range was directly extracted from IUCN polygons.