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PremiseLarge‐scale projects such as the National Ecological Observatory Network (NEON) collect ecological data on entire biomes to track climate change. NEON provides an opportunity to launch community transcriptomic projects that ask integrative questions in ecology and evolution. We conducted a pilot study to investigate the challenges of collecting RNA‐seq data from diverse plant communities. MethodsWe generated >650 Gbp of RNA‐seq for 24 vascular plant species representing 12 genera and nine families at the Harvard Forest NEON site. Each species was sampled twice in 2016 (July and August). We assessed transcriptome quality and content with TransRate, BUSCO, and Gene Ontology annotations. ResultsOnly modest differences in assembly quality were observed across multiplek‐mers. On average, transcriptomes contained hits to >70% of loci in the BUSCO database. We found no significant difference in the number of assembled and annotated transcripts between diploid and polyploid transcriptomes. DiscussionWe provide new RNA‐seq data sets for 24 species of vascular plants in Harvard Forest. Challenges associated with this type of study included recovery of high‐quality RNA from diverse species and access to NEON sites for genomic sampling. Overcoming these challenges offers opportunities for large‐scale studies at the intersection of ecology and genomics. 

PremiseTagSeq is a cost‐effective approach for gene expression studies requiring a large number of samples. To date, TagSeq studies in plants have been limited to those with a high‐quality reference genome. We tested the suitability of reference transcriptomes for TagSeq in non‐model plants, as part of a study of natural gene expression variation at the Santa Rita Experimental Range National Ecological Observatory Network (NEON) core site. MethodsTissue for TagSeq was sampled from multiple individuals of four species (Bouteloua aristidoidesandEragrostis lehmanniana[Poaceae],Tidestromia lanuginosa[Amaranthaceae], andParkinsonia florida[Fabaceae]) at two locations on three dates (56 samples total). One sample per species was used to create a reference transcriptome via standard RNA‐seq. TagSeq performance was assessed by recovery of reference loci, specificity of tag alignments, and variation among samples. ResultsA high fraction of tags aligned to each reference and mapped uniquely. Expression patterns were quantifiable for tens of thousands of loci, which revealed consistent spatial differentiation in expression for all species. DiscussionTagSeq using de novo reference transcriptomes was an effective approach to quantifying gene expression in this study. Tags were highly locus specific and generated biologically informative profiles for four non‐model plant species.