Determinants of flammability in savanna grass species

1. Tropical grasses fuel the majority of fires on Earth. In fire-prone landscapes, enhanced flammabil-ity may be adaptive for grasses via the maintenance of an open canopy and an increase in spa-tiotemporal opportunities for recruitment and regeneration. In addit ion, by burning intensely butbriefly, high flammability may protect resprouting buds from lethal temperatures. Despite thesepotential benefits of high flammability to fire-prone grasses, variation in flammability among grassspecies, and how trait differences underpin this variation, remains unknown.2. By burning leaves and plant parts, we experimentally determined how five plant traits (biomassquantity, biomass density, biomass moisture content, leaf surface-area-to-volume ratio and leaf effec-tive heat of combustion) combined to determine the three components of flammability (ignitability,sustainability and combustibility) at the leaf and plant scales in 25 grass species of fire-pr one SouthAfrican grasslands at a time of peak fire occurrence. The influence of evolutionary history onflammability was assessed based on a phylogeny built here for the study species.3. Grass speci es differed significantly in all components of flammability. Accounting for evolution-ary history helped to explain patterns in leaf-scale combustibility and sustainability. The five mea-sured plant traits predicted components of flammability, particularly leaf ignitability and plantcombustibility in which 70% and 58% of variation, respectively, could be explained by a combina-tion of the traits. Total above-ground biomass was a key drive r o f combustibility and sustainabi litywith high biomass species burning more intensely and for longer, and producing the highest pre-dicted fire spread rates. Moisture content was the main influence on ignitability, where speci es withhigher moisture conten ts took longer to ignite and once alight burnt at a slower rate. Bioma ss den-sity, leaf surface-area-to-volume ratio and leaf effective heat of combustion were weaker predictorsof flammability components.4. Synthesis. We demonstrate that grass flammability is predicted from easily measurable plant func-tional traits and is influenced by evolutionary history with some components showing phylogeneticsignal. Grasses are not homogenous fuels to fire. Rather, species differ in functional traits that inturn demonstrably influence flammability. This diver sity is consistent with the idea that flammabilitymay be an adaptive trait for grasses of fire-prone ecosystems

Estimation of Genetic Correlation via Linkage Disequilibrium Score Regression and Genomic Restricted Maximum Likelihood

J. Lönnqvist on työryhmän Psychiat Genomics Consortium jäsen.Genetic correlation is a key population parameter that describes the shared genetic architecture of complex traits and diseases. It can be estimated by current state-of-art methods, i.e., linkage disequilibrium score regression (LDSC) and genomic restricted maximum likelihood (GREML). The massively reduced computing burden of LDSC compared to GREML makes it an attractive tool, although the accuracy (i.e., magnitude of standard errors) of LDSC estimates has not been thoroughly studied. In simulation, we show that the accuracy of GREML is generally higher than that of LDSC. When there is genetic heterogeneity between the actual sample and reference data from which LD scores are estimated, the accuracy of LDSC decreases further. In real data analyses estimating the genetic correlation between schizophrenia (SCZ) and body mass index, we show that GREML estimates based on similar to 150,000 individuals give a higher accuracy than LDSC estimates based on similar to 400,000 individuals (from combinedmeta-data). A GREML genomic partitioning analysis reveals that the genetic correlation between SCZ and height is significantly negative for regulatory regions, which whole genome or LDSC approach has less power to detect. We conclude that LDSC estimates should be carefully interpreted as there can be uncertainty about homogeneity among combined meta-datasets. We suggest that any interesting findings from massive LDSC analysis for a large number of complex traits should be followed up, where possible, with more detailed analyses with GREML methods, even if sample sizes are lesser.Peer reviewe