Ecophysiological Models of Forest Stand Dynamics

It is well known that woodlands play a crucial role in stabilizing the natural environment. They greatly influence and regulate hydraulic cycles, and thus the flow of waters and local humidity conditions. They also filter air pollutants, thus protecting vulnerable soils and water bodies within forested watersheds. Therefore, as global belts of Boreal, Moderate and Tropical Forests actively take part in different biogeochemical and physical cycles in the biosphere, and play an extremely important role in the exchange of heat and moisture between the atmosphere and continents, an assessment of the forests in different time and spatial scales is of considerable value to the life of the human society. The systems approach permits us to look at these interrelationships in a comprehensive way and to see many negative and positive feedbacks which, together, provide a dynamical equilibrium of the waves in the all forest belts mentioned above (including others organic and inorganic waves, such as waves of insects, diseases, fires etc.). In the course of its existence IlASA has constantly been occupied with different aspects of the forest life. From time to time international working groups are formed on a IlASA base to examine the different aspects of the forest and forestry dynamics. The most recent example is a book on systems analysis of the Boreal Forest Dynamics, published by Cambridge University Press (Shugart et al., eds., September 1991). A group of American, European, Canadian and Soviet authors have worked together through a collaborative network. The products of the group include a general boreal forest model (which is currently being used to evaluate the potential effects of global climate change on the North American Boreal Zone); models on fire dynamics, seed dispersal, permafrost dynamics, herbivory and CO2 flux have been developed, providing a general modeling framework for simulating patterns and processes in the boreal zone. The present paper may be considered as some additional input to the problem, in the form of Ecophysiological Models, which were partially missing in the above-mentioned book. The paper partially intersects with the contents of the book, but from a different angle, especially as many papers considering the Russian view of the problem are added. The book on "System Analysis of the Boreal Forest Dynamics" and this outline stress the necessity of the development of a collaborative research effort to continue the development of computer models of the boreal forest (analogue to the GCM -- see, for example, Shugart, Bonan), and the so-called analytical models (analogue to the Global Average Models (GAM) -- see for example Antonovsky, Korzukhin) in response to environmental change. Assessments of anthropogenic stress on forests that show such complex dynamics are daunting. There is a clear need for a continuation of process-oriented comparative studies in polluted and non-polluted regions of the boreal forests to better understand these effects. It is clear from the reviews of actual observations and experimental evidence from the boreal forest and from the boreal forest models that the landscape response of boreal forests to stress is complex and not easily obtained from static measurements. Furthermore, the feedback complexities in the boreal forest ecosystem suggest that a multiple research program of experimentation, modeling and observation may lead to a better understanding of the forest dynamics under stress or novel situations than one-dimensional research programs

A probabilistic model for gene content evolution with duplication, loss, and horizontal transfer

We introduce a Markov model for the evolution of a gene family along a phylogeny. The model includes parameters for the rates of horizontal gene transfer, gene duplication, and gene loss, in addition to branch lengths in the phylogeny. The likelihood for the changes in the size of a gene family across different organisms can be calculated in O(N+hM^2) time and O(N+M^2) space, where N is the number of organisms, $h$ is the height of the phylogeny, and M is the sum of family sizes. We apply the model to the evolution of gene content in Preoteobacteria using the gene families in the COG (Clusters of Orthologous Groups) database

Analyitical approach to ecophysiological forest modeling: computer reference-information system

Consiglio Nazionale delle Ricerche (CNR). Biblioteca Centrale / CNR - Consiglio Nazionale delle RichercheSIGLEITItal

Measurement of single-diffractive dijet production in proton–proton collisions at √s=8Te with the CMS and TOTEM experiments

Measurements are presented of the single-diffractive dijet cross section and the diffractive cross section as a function of the proton fractional momentum loss ξ and the four-momentum transfer squared t. Both processes pp→pX and pp→Xp, i.e. with the proton scattering to either side of the interaction point, are measured, where X includes at least two jets; the results of the two processes are averaged. The analyses are based on data collected simultaneously with the CMS and TOTEM detectors at the LHC in proton–proton collisions at s=8Te during a dedicated run with β∗=90m at low instantaneous luminosity and correspond to an integrated luminosity of 37.5nb-1. The single-diffractive dijet cross section σjjpX, in the kinematic region ξ< 0.1 , 0.03<|t|<1Ge2, with at least two jets with transverse momentum pT>40Ge, and pseudorapidity | η| < 4.4 , is 21.7±0.9(stat)-3.3+3.0(syst)±0.9(lumi)nb. The ratio of the single-diffractive to inclusive dijet yields, normalised per unit of ξ, is presented as a function of x, the longitudinal momentum fraction of the proton carried by the struck parton. The ratio in the kinematic region defined above, for x values in the range - 2.9 ≤ log 10x≤ - 1.6 , is R=(σjjpX/Δξ)/σjj=0.025±0.001(stat)±0.003(syst), where σjjpX and σjj are the single-diffractive and inclusive dijet cross sections, respectively. The results are compared with predictions from models of diffractive and nondiffractive interactions. Monte Carlo predictions based on the HERA diffractive parton distribution functions agree well with the data when corrected for the effect of soft rescattering between the spectator partons. © 2020, CERN for the benefit of the CMS and TOTEM collaborations