Many prior studies have uncovered evidence for local adaptation using reciprocal transplant experiments. However, these studies are rarely conducted for a long enough time to observe succession and competitive dynamics in a community context, limiting inferences for long‐lived species. Furthermore, the genetic basis of local adaptation and genetic associations with climate has rarely been identified. Here, we report on a long‐term (6‐year) experiment conducted under natural conditions focused on Andropogon gerardii, the dominant grass of the North American Great Plains tallgrass ecosystem. We focus on this foundation grass that comprises 80% of tallgrass prairie biomass and is widely used in 20,000 km2 of restoration. Specifically, we asked the following questions: (a) Whether ecotypes are locally adapted to regional climate in realistic ecological communities. (b) Does adaptive genetic variation underpin divergent phenotypes across the climate gradient? (c) Is there evidence of local adaptation if the plants are exposed to competition among ecotypes in mixed ecotype plots? Finally, (d) are local adaptation and genetic divergence related to climate? Reciprocal gardens were planted with 3 regional ecotypes (originating from dry, mesic, wet climate sources) of Andropogon gerardii across a precipitation gradient (500–1,200 mm/year) in the US Great Plains. We demonstrate local adaptation and differentiation of ecotypes in wet and dry environments. Surprisingly, the apparent generalist mesic ecotype performed comparably under all rainfall conditions. Ecotype performance was underpinned by differences in neutral diversity and candidate genes corroborating strong differences among ecotypes. Ecotype differentiation was related to climate, primarily rainfall. Without long‐term studies, wrong conclusions would have been reached based on the first two years. Further, restoring prairies with climate‐matched ecotypes is critical to future ecology, conservation, and sustainability under climate change.
Big bluestem (Andropogon gerardii) is an ecologically dominant grass with wide distribution across the environmental gradient of U.S. Midwest grasslands. This system offers an ideal natural laboratory to study population divergence and adaptation in spatially varying climates. Objectives were to: (i) characterize neutral genetic diversity and structure within and among three regional ecotypes derived from 11 prairies across the U.S. Midwest environmental gradient, (ii) distinguish between the relative roles of isolation by distance (IBD) vs. isolation by environment (IBE) on ecotype divergence, (iii) identify outlier loci under selection and (iv) assess the association between outlier loci and climate. Using two primer sets, we genotyped 378 plants at 384 polymorphic AFLP loci across regional ecotypes from central and eastern Kansas and Illinois. Neighbour-joining tree and PCoA revealed strong genetic differentiation between Kansas and Illinois ecotypes, which was better explained by IBE than IBD. We found high genetic variability within prairies (80%) and even fragmented Illinois prairies, surprisingly, contained high within-prairie genetic diversity (92%). Using Bayenv2, 14 top-ranked outlier loci among ecotypes were associated with temperature and precipitation variables. Six of seven BayeScanFST outliers were in common with Bayenv2 outliers. High genetic diversity may enable big bluestem populations to better withstand changing climates; however, population divergence supports the use of local ecotypes in grassland restoration. Knowledge of genetic variation in this ecological dominant and other grassland species will be critical to understanding grassland response and restoration challenges in the face of a changing climate.
This is an open access article under the terms of the Creat ive Commo ns Attri butio n-NonCo mmerc ial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
BackgroundDifferential expression (DE) analysis of RNA-seq data still poses inferential challenges, such as handling of transcripts characterized by low expression levels. In this study, we use a plasmode-based approach to assess the relative performance of alternative inferential strategies on RNA-seq transcripts, with special emphasis on transcripts characterized by a small number of read counts, so-called low-count transcripts, as motivated by an ecological application in prairie grasses. Big bluestem (Andropogon gerardii) is a wide-ranging dominant prairie grass of ecological and agricultural importance to the US Midwest while edaphic subspecies sand bluestem (A. gerardii ssp. Hallii) grows exclusively on sand dunes. Relative to big bluestem, sand bluestem exhibits qualitative phenotypic divergence consistent with enhanced drought tolerance, plausibly associated with transcripts of low expression levels. Our dataset consists of RNA-seq read counts for 25,582 transcripts (60 % of which are classified as low-count) collected from leaf tissue of individual plants of big bluestem (n = 4) and sand bluestem (n = 4). Focused on low-count transcripts, we compare alternative ad-hoc data filtering techniques commonly used in RNA-seq pipelines and assess the inferential performance of recently developed statistical methods for DE analysis, namely DESeq2 and edgeR robust. These methods attempt to overcome the inherently noisy behavior of low-count transcripts by either shrinkage or differential weighting of observations, respectively.ResultsBoth DE methods seemed to properly control family-wise type 1 error on low-count transcripts, whereas edgeR robust showed greater power and DESeq2 showed greater precision and accuracy. However, specification of the degree of freedom parameter under edgeR robust had a non-trivial impact on inference and should be handled carefully. When properly specified, both DE methods showed overall promising inferential performance on low-count transcripts, suggesting that ad-hoc data filtering steps at arbitrary expression thresholds may be unnecessary. A note of caution is in order regarding the approximate nature of DE tests under both methods.ConclusionsPractical recommendations for DE inference are provided when low-count RNA-seq transcripts are of interest, as is the case in the comparison of subspecies of bluestem grasses. Insights from this study may also be relevant to other applications focused on transcripts of low expression levels.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2442-7) contains supplementary material, which is available to authorized users.
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