Exploration of the Eucalyptus globulus gene pool

The first Europeans to discover Eucalyptus globulus were French explorers in 1792. Its seed was rapidly spread throughout the world in the 19th century and this was the species by which much of the world first knew the genus. However, it was in the industrial forests of the 20th century that this species, once considered the ‘Prince of Eucalypts’, achieved greatest prominence due to its fast growth and superior pulp qualities. Formal breeding first commenced in 1966 in Portugal and in the late 1980’s large base population trials from open-pollinated seed collections from native stands were established in many countries. These trials have provided unprecedented insights into the quantitative genetic control of numerous traits of economic and ecological importance and how this variation is spatially distributed in the native range of the species. However with large, fully pedigreed breeding populations becoming available for quantitative analysis and the rapidly expanding knowledge of DNA sequence variation, we are now at the threshold of a new understanding of this important eucalypt gene pool. Indications of the significance of non-additive genetic effects are becoming available. The E. globulus chloroplast genome has now been sequenced and several genome maps have been published. Studies of the variation in nuclear microsatellites and the lignin biosynthesis gene CCR confirm the complex, spatially structured nature of the native gene pool. Strong spatial structuring of the chloroplast genome has provided a tool for tracking seed migration and the geographic origin of exotic landraces. Highly divergent lineages of chloroplast DNA have been discovered and studies of the hypervariable JLA+ region argue that some components of the E. globulus gene pool have been assimilated from other species following hybridisation

Structure Factors and Their Distributions in Driven Two-Species Models

We study spatial correlations and structure factors in a three-state stochastic lattice gas, consisting of holes and two oppositely ``charged'' species of particles, subject to an ``electric'' field at zero total charge. The dynamics consists of two nearest-neighbor exchange processes, occuring on different times scales, namely, particle-hole and particle-particle exchanges. Using both, Langevin equations and Monte Carlo simulations, we study the steady-state structure factors and correlation functions in the disordered phase, where density profiles are homogeneous. In contrast to equilibrium systems, the average structure factors here show a discontinuity singularity at the origin. The associated spatial correlation functions exhibit intricate crossovers between exponential decays and power laws of different kinds. The full probability distributions of the structure factors are universal asymmetric exponential distributions.Comment: RevTex, 18 pages, 4 postscript figures included, mistaken half-empty page correcte

Highlighting when animals expend excessive energy for travel using dynamic body acceleration

Travel represents a major cost for many animals so there should be selection pressure for it to be efficient – at minimum cost. However, animals sometimes exceed minimum travel costs for reasons that must be correspondingly important. We use Dynamic Body Acceleration (DBA), an acceleration-based metric, as a proxy for movement-based power, in tandem with vertical velocity (rate of change in depth) in a shark (Rhincodon typus) to derive the minimum estimated power required to swim at defined vertical velocities. We show how subtraction of measured DBA from the estimated minimum power for any given vertical velocity provides a “proxy for power above minimum” metric (PPAmin), highlighting when these animals travel above minimum power. We suggest that the adoption of this metric across species has value in identifying where and when animals are subject to compelling conditions that lead them to deviate from ostensibly judicious energy expenditure

Understanding and predicting animal movements and distributions in the Anthropocene

1. Predicting animal movements and spatial distributions is crucial for our comprehension of ecological processes and provides key evidence for conserving and managing populations, species and ecosystems. Notwithstanding considerable progress in movement ecology in recent decades, developing robust predictions for rapidly changing environments remains challenging. 2. To accurately predict the effects of anthropogenic change, it is important to first identify the defining features of human-modified environments and their consequences on the drivers of animal movement. We review and discuss these features within the movement ecology framework, describing relationships between external environment, internal state, navigation and motion capacity. 3. Developing robust predictions under novel situations requires models moving beyond purely correlative approaches to a dynamical systems perspective. This requires increased mechanistic modelling, using functional parameters derived from first principles of animal movement and decision-making. Theory and empirical observations should be better integrated by using experimental approaches. Models should be fitted to new and historic data gathered across a wide range of contrasting environmental conditions. We need therefore a targeted and supervised approach to data collection, increasing the range of studied taxa and carefully considering issues of scale and bias, and mechanistic modelling. Thus, we caution against the indiscriminate non-supervised use of citizen science data, AI and machine learning models. 4. We highlight the challenges and opportunities of incorporating movement predictions into management actions and policy. Rewilding and translocation schemes offer exciting opportunities to collect data from novel environments, enabling tests of model predictions across varied contexts and scales. Adaptive management frameworks in particular, based on a stepwise iterative process, including predictions and refinements, provide exciting opportunities of mutual benefit to movement ecology and conservation. 5. In conclusion, movement ecology is on the verge of transforming from a descriptive to a predictive science. This is a timely progression, given that robust predictions under rapidly changing environmental conditions are now more urgently needed than ever for evidence-based management and policy decisions. Our key aim now is not to describe the existing data as well as possible, but rather to understand the underlying mechanisms and develop models with reliable predictive ability in novel situations

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Carbon revenues and economic breeding objectives in Eucalyptus globulus pulpwood plantations

This paper investigates the integration of carbon revenues into production system models used to define economic breeding objectives for the genetic improvement of Eucalyptus globulus pulp-wood plantations. A model was used to estimate that carbon dioxide equivalent accumulation in biomass in the Australian Eucalyptus globulus plantation estate established between 2004 and 2012 was in the order of ~146 t CO₂e ha⁻¹, of which 62 t CO₂e ha⁻¹ were tradable in 2012 and a further 30 t CO₂e ha⁻¹ were tradable in 2016. By considering a system where revenues for carbon sequestration are directly dependant upon biomass production in a plantation, it was possible to determine whether economic breeding objectives for the genetic improvement of E. globulus will be sensitive to the revenue from carbon sequestration. The correlated response of breeding objectives with and without carbon ( ΔcGH₁ ) never fell below 0.86 in sensitivity analysis, and the mean was 0.93. As such, where economic breeding objectives for the genetic improvement of Eucalyptus globulus for pulpwood plantations are based on maximizing NPV by increasing biomass production, the consideration of carbon in economic breeding objectives will provide no significant gains in NPV