The Magnitude of Local Host Specif icity for Phytophagous Insects and its Implications for Estimates of Global Species Richness

Abstract: Estimates of the global number of arthropod species range from 2 million to more than 30 million species. One of the most critical assumptions affecting the higher of these estimates is the assumed magnitude of host specificity of phytophagous insects, which varies considerably. Difficulties in estimating this value are caused by both lack of satisfactory data sets and lack of methods for its objective calculation. We provide a new method for predicting host specificity for phytophagous insects at a local scale based on the concept of effective specialization. When the insect-plant host associations of a restricted number of plant species are known, we can predict the magnitude of host specificity when more plant species are included. Based on 2561 host observations of 697 beetle species on 50 plant species in the canopies of a tropical dry forest in Panama, we predict that host specificity for adult phytophagous beetles in this forest range from 7% to 10%. These values suggest that the higher estimates of global species richness are not tenable.     

Disentangling the effects of heterogeneity, stochastic dynamics and sampling in a community of aquatic insects

A dynamic and heterogeneous species abundance model generating the lognormal species abundance distribution is fitted to time series of species data from an assemblage of stoneflies and mayflies (Plecoptera and Ephemeroptera) of an aquatic insect community collected over a period of 15 years. In each year except one, we analyze 5 parallel samples taken at the same time of the season giving information about the over-dispersion in the sampling relative to the Poisson distribution. Results are derived from a correlation analysis, where the correlation in the bivariate normal distribution of log abundance is used as measurement of similarity between communities. The analysis enables decomposition of the variance of the lognormal species abundance distribution into three components due to heterogeneity among species, stochastic dynamics driven by environmental noise, and over-dispersion in sampling, accounting for 62.9, 30.6 and 6.5% of the total variance, respectively. Corrected for sampling the heterogeneity and stochastic components accordingly account for 67.3 and 32.7% of the among species variance in log abundance. By using this method, it is possible to disentangle the effect of heterogeneity and stochastic dynamics by quantifying these components and correctly remove sampling effects on the observed species abundance distribution.