A Comprehensive Map of Mobile Element Insertion Polymorphisms in Humans

As a consequence of the accumulation of insertion events over evolutionary time, mobile elements now comprise nearly half of the human genome. The Alu, L1, and SVA mobile element families are still duplicating, generating variation between individual genomes. Mobile element insertions (MEI) have been identified as causes for genetic diseases, including hemophilia, neurofibromatosis, and various cancers. Here we present a comprehensive map of 7,380 MEI polymorphisms from the 1000 Genomes Project whole-genome sequencing data of 185 samples in three major populations detected with two detection methods. This catalog enables us to systematically study mutation rates, population segregation, genomic distribution, and functional properties of MEI polymorphisms and to compare MEI to SNP variation from the same individuals. Population allele frequencies of MEI and SNPs are described, broadly, by the same neutral ancestral processes despite vastly different mutation mechanisms and rates, except in coding regions where MEI are virtually absent, presumably due to strong negative selection. A direct comparison of MEI and SNP diversity levels suggests a differential mobile element insertion rate among populations

Biogeographically distinct controls on C₃ and C₄ grass distributions: merging community and physiological ecology

AIM: C₄ photosynthesis is an adaptation that maintains efficient carbon assimilation in warm and low-CO₂ conditions. Due to the importance of C₄ grasses for carbon and surface energy fluxes numerous models have been proposed to describe their spatial distribution and forecast responses to climate change. These models often rely on broad climate predictors (e.g., temperature and precipitation) but fail to integrate other ecologically relevant factors, such as disturbance and competition, which may modify realized C₃/C₄ grass distributions. We evaluate the contribution of ecological factors, in addition to climate predictors, to C₃/C₄ grass distributions across multiple biogeographic regions of North America in a multi-source database of >40,000 vegetation plots. LOCATION: Conterminous United States of America (USA). METHODS: We identified a comprehensive pool of physiological-climatic models in the literature and used information theoretic criteria to select a primary physiological predictor of C₃ and C₄ grasses. Subsequently, the climate model was combined with ecological predictors using a multiple regression framework and tested within eight regions within the USA. RESULTS: Surprisingly, grass-dominated communities across the USA exist largely in a C₃ or C₄ dominated state. Transitions between C₃/C₄ dominance were best explained by models that integrated temperature and precipitation with ecological factors that varied according to region. For some regions, such as Eastern Temperate Forests, local, ecological factors were comparable in strength to broad climate predictors of C₃/C₄ abundance. MAIN CONCLUSION: Local, ecological factors modify C₃/C₄ grass responses to broad-scale climatic drivers in ways that manifest at regional scales. In Eastern Temperate Forests, for example, C₄ grass abundances are maintained below climatic expectations where tree cover creates light limitation, but above expectations where frequent fires reduce tree cover. Thus, local ecological factors contribute to major among-region differences in the climate responses of C₃/C₄ grasses.Keywords: Invasive, C₄, Fire, Biogeography, Tree cover, C₃, Crossover temperatur

Comment on “The global tree restoration potential”

Bastin et al.’s estimate (Reports, 5 July 2019, p. 76) that tree planting for climate change mitigation could sequester 205 gigatonnes of carbon is approximately five times too large. Their analysis inflated soil organic carbon gains, failed to safeguard against warming from trees at high latitudes and elevations, and considered afforestation of savannas, grasslands, and shrublands to be restoration.Funding: Supported by the Texas A&M Sid Kyle Global Savanna Research Initiative (T.W.B.); Swiss National Science Foundation (20FI20_173691) (N.B.); Centre National pour la Recherche Scientifique CNRS PICS 2018-2020 (RESIGRASS) (E.B.); CNPq (Brazil, 303179/2016-3) (G.D.); CNPq (Brazil) (G.W.F.); CNPq (Brazil, 303988/2018-5) (A.F.); NASA award NNX17AK14G (F.F.); NSF award 1354943 (W.A.H.); Fundação de Amparo à Pesquisa do Estado de Minas Gerais (Brazil, 2016/13232-5) (S.L.S.); the Office of the Royal Society (IC170015) (C.E.R.L.); CNPq (Brazil, 310345/2018-9) (G.E.O.); the Spanish Government (FIROTIC, PGC2018-096569-B-I00) (J.G.P.); the National Research Foundation (ACCESS, 114695) (N.S.); CNPq (Brazil, 303568/2017-8) (F.A.O.S.); NSF awards 1342703 and 1926431 (C.J.S. and D.M.G.); NSF award EAR-1253713 (C.A.E.S.); Deutsche Forschungsgemeinschaft grant 5579 POEM (V.M.T.); and USDA-NIFA Sustainable Agricultural Systems Grant 12726253 (J.W.V.