Next-generation sequencing technologies have provided unprecedented insights into fungal diversity and ecology. However, intrinsic biases and insufficient quality control in next-generation methods can lead to difficult-to-detect errors in estimating fungal community richness, distributions, and composition. The aim of this study was to examine how tissue storage prior to DNA extraction, primer design, and various quality-control approaches commonly used in 454 amplicon pyrosequencing might influence ecological inferences in studies of endophytic and endolichenic fungi. We first contrast 454 data sets generated contemporaneously from subsets of the same plant and lichen tissues that were stored in CTAB buffer, dried in silica gel, or freshly frozen prior to DNA extraction. We show that storage in silica gel markedly limits the recovery of sequence data and yields a small fraction of the diversity observed by the other two methods. Using lichen mycobiont sequences as internal positive controls, we next show that despite careful filtering of raw reads and utilization of current best-practice OTU clustering methods, homopolymer errors in sequences representing rare taxa artificially increased estimates of richness ca. 15-fold in a model data set. Third, we show that inferences regarding endolichenic diversity can be improved by using a novel primer that reduces amplification of the mycobiont. Together, our results provide a rationale for selecting tissue treatment regimes prior to DNA extraction, demonstrate the efficacy of reducing mycobiont amplification in studies of the fungal microbiomes of lichen thalli, and highlight the difficulties in differentiating true information about fungal biodiversity from methodological artifacts.