Data from: Comparing methods for mapping global parasite diversity - Pappalardo, Paula; Morales-Castilla, Ignacio; Park, Andrew; Huang, Shan; Schmidt, John; Stephens, Patrick (Dryad, 2019)
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.
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
Keywords: mapping; infectious disease; pathogen; mammalian host; Cartography; geographic range