How much leaf area do insects eat? A data set of insect herbivory sampled globally with a standardized protocol

Herbivory is ubiquitous. Despite being a potential driver of plant distribution and performance, herbivory remains largely undocumented. Some early attempts have been made to review, globally, how much leaf area is removed through insect feeding. Kozlov et al., in one of the most comprehensive reviews regarding global patterns of herbivory, have compiled published studies regarding foliar removal and sampled data on global herbivory levels using a standardized protocol. However, in the review by Kozlov et al., only 15 sampling sites, comprising 33 plant species, were evaluated in tropical areas around the globe. In Brazil, which ranks first in terms of plant biodiversity, with a total of 46,097 species, almost half (43%) being endemic, a single data point was sampled, covering only two plant species. In an attempt to increase knowledge regarding herbivory in tropical plant species and to provide the raw data needed to test general hypotheses related to plant–herbivore interactions across large spatial scales, we proposed a joint, collaborative network to evaluate tropical herbivory. This network allowed us to update and expand the data on insect herbivory in tropical and temperate plant species. Our data set, collected with a standardized protocol, covers 45 sampling sites from nine countries and includes leaf herbivory measurements of 57,239 leaves from 209 species of vascular plants belonging to 65 families from tropical and temperate regions. They expand previous data sets by including a total of 32 sampling sites from tropical areas around the globe, comprising 152 species, 146 of them being sampled in Brazil. For temperate areas, it includes 13 sampling sites, comprising 59 species

A novel method to predict dark diversity using unconstrained ordination analysis

[Questions] Species pools are the product of complex ecological and evolutionary mechanisms, operating over a range of spatial scales. Here, we focus on species absent from local sites but with the potential to establish within communities — known as dark diversity. Methods for estimating dark diversity are still being developed and need to be compared, as well as tested for the type, and amount, of reference data needed to calibrate these methods. [Location] South Bohemia (48°58′ N, 14°28′ E) and Železné Hory (49°52′ N, 15°34′ E), Czech Republic. [Method] We compared a widely accepted algorithm to estimate species pools (Beals smoothing index, based on species co-occurrence) against a novel method based on an unconstrained ordination (UNO). Following previous work, we used spatially nested sampling for target plots, with the dark diversity estimates computed from smaller plots validated against additional species present in larger plots, and a reference dataset (Czech National Phytosociological Database of >30,000 plots as global reference data). We determined which method provides the best estimate of dark diversity with an index termed the “Success Rate Index”. [Results] When using the whole reference dataset (national scale), both UNO and Beals provided comparable predictions of dark diversity that were better than null expectations based on species frequency. However, when predicting from regionally restricted spatial scales, UNO performed significantly better than Beals. UNO also tended to detect less common species better than Beals. The success rate of combining UNO and Beals slightly outperformed the results obtained from the single methods, but only with the largest reference dataset. [Conclusions] The UNO method provides a consistently reliable estimate of dark diversity, particularly when the reference dataset is size-limited. For future calculations, we urge caution regarding the choice of dark diversity methods with respect to the reference data available, and how different methods handle species of high, and low, occurrence frequency.Grant Agency of the Czech Republic grant (GA16‐15012S; to Francesco de Bello and Lars Götzenberger). Long‐term research development grant from the Czech Academy of Sciences (RVO 67985939; FdB and LG). Czech Science Foundation ‐ Centre of Excellence PLADIAS (14‐36079G; Petr Šmilauer and Jan Lepš)