A comparison of diversity estimators applied to a database of host–parasite associations

Abstract

Understanding the drivers of biodiversity is important for forecasting changes in the distribution of life on earth. However, most studies of biodiversity are limited by uneven sampling effort, with some regions or taxa better sampled than others. Numerous methods have been developed to account for differences in sampling effort, but most methods were developed for systematic surveys in which all study units are sampled using the same design and assemblages are sampled randomly. Databases compiled from multiple sources, such as from the literature, often violate these assumptions because they are composed of studies that vary widely in their goals and methods. Here, we compared the performance of several popular methods for estimating parasite diversity based on a large and widely used parasite database, the Global Mammal Parasite Database (GMPD). We created artificial datasets of host–parasite interactions based on the structure of the GMPD, then used these datasets to evaluate which methods best control for differential sampling effort. We evaluated the precision and bias of seven methods, including species accumulation and nonparametric diversity estimators, compared to analyzing the raw data without controlling for sampling variation. We find that nonparametric estimators, and particularly the Chao2 and second‐order jackknife estimators, perform better than other methods. However, these estimators still perform poorly relative to systematic sampling, and effect sizes should be interpreted with caution because they tend to be lower than actual effect sizes. Overall, these estimators are more effective in comparative studies than for producing true estimates of diversity. We make recommendations for future sampling strategies and statistical methods that would improve estimates of global parasite diversity.

Epidemiological transitions in human evolution and the richness of viruses, helminths, and protozoa

“…These factors contribute to higher awareness of a broader diversity of parasite species in humans than in other hosts. Although we applied ecological methods to control for study effort [ 11 ] and focused on country-level data, human parasite research—and thus, discovery—may be biased in unique ways that are not fully addressed through the use of these methods. For example, given interests in public health, research has devoted much effort to resolving viral lineages in humans, which could inflate measures of viral richness.…”

“…The Chao2 method estimates species richness from patterns of incidence across sampling events [ 30 ]. Based on analysis of artificial datasets simulated to mimic the structure of the Global Mammal Parasite Database, we previously found that estimating richness using Chao2 is relatively more precise and unbiased than other methods of controlling for uneven sampling effort across species, and especially useful for making comparisons across species [ 11 ]. In this study, we thus use an estimate of species richness that is based on patterns of parasite species discovery in different studies, rather than an observed value (although we refer to this value as “observed” to draw a contrast with the predicted values, see below).…”

Primate malarias as a model for cross-species parasite transmission

“…For example, terrestrial primate species are more likely to be sampled for parasites than arboreal primate species ( Cooper and Nunn, 2013 ; Poulin et al, 2016 ). Thus, a variety of approaches have been developed to deal with variation in sampling effort ( Nunn et al, 2003 ; Elmasri et al, 2020 ; Teitelbaum et al, 2020 ; Amoroso and Nunn, 2021 ).…”

Urban-adapted mammal species have more known pathogens

“…This relationship could explain the common observation that fast-lived host species (i.e., those that favour reproduction over survival) tend to disproportionately source zoonotic parasites (Keesing et al 2010;Ostfeld et al 2014;. Complicating matters, a given species' observed parasite diversity depends inherently on the effort that has been directed towards examining it (Olival et al 2017;Gutiérrez et al 2019;Teitelbaum et al 2019;Mollentze & Streicker 2020). Such research effort is heterogeneously distributed in space (Allen et al 2017;Olival et al 2017;Jorge & Poulin 2018) and across mammal species, particularly with regards to life history and taxonomy (Olival et al 2017;Mollentze & Streicker 2020).…”