DOI: 10.5061/dryad.rs76vv7
title: Data from: Comparing methods for mapping global parasite diversity

description: Aim Parasites are a major component of global ecosystems, yet spatial
 variation in parasite diversity is poorly known, largely because their
 occurrence data are limited and thus difficult to interpret. Using a
 recently compiled database of parasite occurrences, we compare different
 models which we use to infer parasite geographic ranges and parasite
 species richness across the globe. Innovation To date, most studies
 exploring spatial patterns of parasite diversity assumed, with little
 validation, that the geographic range of a parasite species can be
 represented by the collective geographic range of its host species. Our
 study compares this assumption with a suite of other methods to infer
 parasite distribution from parasite occurrence data (e.g. based on data
 density, ecoregions and climatic conditions). We highlight diversity
 hotspots identified by the various methods and compare the effects of
 sampling intensities in different regions, a crucial factor of observed
 parasite diversity. Main conclusions The type of model used to infer
 parasite distributions affects estimates of both total species richness
 and spatial patterns of hotspots of parasite richness. Overall, the models
 based on reported occurrences share similar areas of high parasite
 richness that tends to be biased towards areas of high sampling effort. In
 contrast, the model based on host distributions showed hotspots of
 parasite diversity which are biased towards areas of high host species
 richness. Accounting for sampling effort could only help to reconcile the
 outcome from the different models in some regions. Further, the
 non-saturated species accumulation curves even for the best studied
 regions of the world such as Europe and North America as a call for
 further sampling effort and development of effective analytic tools that
 can provide robust accounts of global parasite diversity.
description: Parasite occurrence data
 used in our studyThe final database used
 in our study includes a subset of parasite geographic occurrences from
 Stephens et al. (2017; Ecology 98: 1476. doi: 10.1002/ecy.1799) and
 geographic occurrences we georeferenced from information published in
 Correa et al. (2016; Revista Mexicana de Biodiversidad, 87, 908-918.).
 From Stephens et al. (2017) we used only terrestrial hosts with full
 binomial names for host and parasite and included only the parasite
 occurrences that fell within 50km of the IUCN host geographic range.
 Additional filters and modifications from Stephens et al. (2017) are
 described in Appendix S1.cleanedOccurrences_SuppMat.csvAppendix S9: R functionsThis R file includes all the functions used for cleaning, filtering and manipulating data, together with the functions used for data analysis. Each function has a short description of its goal and arguments.AppendixS9_R_Functions_Pappalardo_etal.RAppendix S3: Parasite diversity maps based on a simple climatic modelThis rmarkdown file includes the code used to generate Appendix S3, that describes how we generated parasite diversity maps based on simple climatic modelsAppendix_S3_ClimaticModel_Pappalardo_etal.RmdAppendix S8: Parasite richness analysis for carnivores and ungulates in the continental USThis rmarkdown file includes the code used to generate Appendix S8, that describes how we generated parasite diversity maps for the continental United States based on different methods to estimate parasite geographic range.Appendix_S8_RegionalAnalysis_US_Pappalardo_etal.RmdAppendix S1: Generating the database used in this study from GMPDv2This rmarkdown file includes the code used to generate Appendix S1, that describes how we generated the datasets used in our study from data in Stephens et al. (2017) and additional geographic occurrences we georeferenced from host-parasite interactions reported in Correa et al. (2016).Appendix_S1_GeneratingDatasets_Pappalardo_etal.Rmd