TRY plant trait database – enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Connecting Molecular Dynamics and Computational Fluid Dynamics

One of the most important developments in the last centuries is the process of miniaturisation and understanding everything that it entails. For the physical sciences this meant the continuing discovery what matter is and how it behaves, while from a practical point of view it meant that more advanced medical, scientific, and consumer applications could be developed. The successful development of miniaturisation requires careful planning, and computer simulations are helping with this aspect. However, continued miniaturisation also lead to several challenges concerning this matter. It is common knowledge that the world around us is made of atoms, however for general macroscopic phenomena, the concept of a continuum works very well. Instead of looking at individual atoms, the behaviour of the collection of a very large number of atoms is studied and is expressed in mathematical equations which can be solved accordingly. On the other hand, recent decades saw a lot of interest in nanotechnology, i.e. the study of phenomenon that happen at the nano-scale (<0.0001\text{ mm}) which means controlling of matter on an atomic and molecular scale. In these cases the concept of a continuum will fail and different techniques must be used to simulate these phenomena. However, there exist an intermediate region (where the typical size is \sim0.00001\mbox{ mm}-\sim0.01\text{ mm}), where both the molecular effects and the continuum effects can be of importance. To simulate this with one single method is very challenging. For example, any method that is very accurate at the small scale will soon be too cumbersome at the large scale. On the other hand, any method that is efficient for the large scale generally lost all the details at the small scale. A solution to this problem is to use both methods at the same time and only apply it to the region where the specific method is most suitable. However, in order for this to work, these methods should communicate with each other. Effectively this means that the methods are coupled and are able to resolve the physical phenomena over a wide range of scales. The subject of this thesis is to develop, implement and test one of these methods, which are generally known as multiscale, coupled, or hybrid methods. In the present work, the Schwarz alternating method is chosen to couple a domain that simulates a dense liquid using molecular dynamics (MD) and a domain that uses continuum methods. The method couples the two domain on the principle that the two domain solve for the same solution in a region where they overlap. Inside this overlap-region the two domain interchange boundary conditions obtained from each other. The boundary conditions for the continuum domain can easily be obtained from the MD simulation results. The specification of boundary conditions on the MD domain is less straightforward. In the current implementation it involves three main steps, which are dealt with accordingly, both for the coupling of one-atom liquids like argon and more complex liquids like water. However, before the coupling can accomplished, it is necessary to obtain more information about where and when a coupling is possible and under which assumptions these coupled simulations give a correct solution. Therefore, the present work also demonstrated several MD simulations/studies to investigate the possibilities and limitations of pure MD simulations, while also the possibilities and limitations of pure continuum methods are studied. The results of the MD simulations showed that large deviations between continuum mechanics and MD are especially noticeable near the solid walls of (nano-sized) channels or near obstacles and are local. These deviations, visible as large variations in the sampled (continuum) macroscopic variables in the MD simulation, are the result of the interaction of the atoms in the liquid with the atoms in the solid wall and can also be observed experimentally. Only far away from the wall the macroscopic variables show their (expected) continuum value without variations. However, although these large variations indicate large non-continuum effects, even for small nano channels of about 5 nm, near continuum-like behaviour can be extracted from the results. This fact was used to determine the viscosity as a function of temperature for four different water models. In general, the results showed that in a channel with a height of about 8 nm yield very good overall continuum-like behaviour. However, this does not yet mean that a pure continuum method to compute the flow inside these channels is advisable. There are also different reasons why not to use a continuum method to simulation certain phenomena, because MD simulations do have some unique benefits, like predicting realistic wall-fluid interactions. Furthermore, with MD several other phenomena can be simulated that are difficult or even impossible with a continuum technique, like the nano-jet and nano-jet breakup. However, care must be taken, because frequently used values of the cutoff radius are too low to accurately model several important phenomena. On the other hand, comparison of continuum results with experimental result showed that, especially the continuum techniques describing electrokinetic effects are reasonably accurate enough, even for a nano-sized device where the height is only 150 nm. The results of several coupled simulations are shown. Here it is explained how the coupled simulation can be seen as a new boundary condition for the continuum, where the value is now more accurately supplied by the communication of the MD and continuum domain. This was demonstrated by simulating Poiseuille flow of argon and water inside a large channel. The non-continuum effects near the wall are simulated accurately by MD and no expensive MD computation time is wasted on the part that resembles a continuum. The coupling of MD and continuum also enabled the specification of non-periodic boundary conditions for MD systems, which are difficult or impossible to implement in a pure MD case. This was demonstrated by a two-dimensional coupled simulation of a nanowire inside a uniform flow of argon. The main benefits and results of this type of simulation are that it investigates the influence on the flow of one obstacle, instead of the one and all its periodic images. The principles behind the coupling of domains can also be applied to other macroscopic variables than velocity, for example temperature. In this work a qualitatively study was performed on a particle inside a temperature gradient field by coupling the MD domain and the continuum domain, effectively investigating thermophoresis in liquids. Finally, a different kind of coupling between molecules and the continuum is explained, which is very efficient to study the behaviour of polymers. For this purpose, a mesoscale simulation to measure the strength of the velocity flux needed to push a polymer into a narrow channel is demonstrated. Here excellent agreement is found with a prediction based on a de Gennes blob model of the polymer; that the critical velocity flux for translocation depends linearly on the temperature, but is independent of the length of the polymer chain or the width of the channel.Process and Energy - Laboratory for Aero & HydrodynamicsMechanical, Maritime and Materials Engineerin

Hydrodynamics of DNA confined in nanoslits and nanochannels

Modeling the dynamics of a confined, semi exible polymer is a challenging problem, owing to the complicated interplay between the configurations of the chain, which are strongly affected by the length scale for the confinement relative to the persistence length of the chain, and the polymer-wall hydrodynamic interactions. At the same time, understanding these dynamics are crucial to the advancement of emerging genomic technologies that use confinement to stretch out DNA and “read” a genomic signature. In this mini-review, we begin by considering what is known experimentally and theoretically about the friction of a wormlike chain such as DNA confined in a slit or a channel. We then discuss how to estimate the friction coefficient of such a chain, either with dynamic simulations or via Monte Carlo sampling and the Kirk-wood pre-averaging approximation. We then review our recent work on computing the diffusivity of DNA in nanoslits and nanochannels, and conclude with some promising avenues for future work and caveats about our approach

TRY plant trait database, enhanced coverage and open access

Plant traits-the morphological, ahawnatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives