The climate change scenarios RCP 4.5 and RCP 8.5, with a representative concentration pathway for stabilization of radiative forcing of 4.5 W m−2 and 8.5 W m−2 by 2100, respectively, predict an ...increase in temperature of 1–4.5° Celsius for Europe and a simultaneous shift in precipitation patterns leading to increased drought frequency and severity. The negative consequences of such changes on tree growth on dry sites or at the dry end of a tree species distribution are well-known, but rarely quantified across large gradients. In this study, the growth of Quercus robur and Quercus petraea (Q. spp.) and Pinus sylvestris in pure and mixed stands was predicted for a historical scenario and the two climate change scenarios RCP 4.5 and RCP 8.5 using the individual tree growth model PrognAus. Predictions were made along an ecological gradient ranging from current mean annual temperatures of 5.5–11.4 °C and with mean annual precipitation sums of 586–929 mm. Initial data for the simulation consisted of 23 triplets established in pure and mixed stands of Q. spp. and P. sylvestris. After doing the simulations until 2100, we fitted a linear mixed model using the predicted volume in the year 2100 as response variable to describe the general trends in the simulation results. Productivity decreased for both Q. spp. and P. sylvestris with increasing temperature, and more so, for the warmer sites of the gradient. P. sylvestris is the more productive tree species in the current climate scenario, but the competitive advantage shifts to Q. spp., which is capable to endure very high negative water potentials, for the more severe climate change scenario. The Q. spp.-P. sylvestris mixture presents an intermediate resilience to increased scenario severity. Enrichment of P. sylvestris stands by creating mixtures with Q. spp., but not the opposite, might be a right silvicultural adaptive strategy, especially at lower latitudes. Tree species mixing can only partly compensate productivity losses due to climate change. This may, however, be possible in combination with other silvicultural adaptation strategies, such as thinning and uneven-aged management.
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•Productivity losses increase with increasing severity of climatic scenario.•Productivity decreases by 7.7 % and 11.6 % for Q. spp. and P. sylvestris for RCP 8.5.•Climate change will shift the competitive advantage from P. sylvestris to Q. spp.•Productivity losses can be mitigated but not compensated by the use of mixtures.•Productivity losses at low latitudes are more severe than at high latitudes.
In central Europe there are many backyard fruit growers who receive no proper education about fruit tree care. Their knowledge is mostly based on various handbooks and learning through trial and ...error. Such learning is slow and can even result in damage to the tree. To shorten the learning time, a new interactive teaching tool EduAPPLE has been developed based on the basic laws of apple tree ( Malus × domestica ) growth and training. Pruning, weighting, tying, and spreading can be interactively practiced over and over again without any danger to the actual trees. Training responses are immediately seen and are analogous to those of real trees. They are not only predetermined by a set of rules, but also calculated based on the changes the actions cause to the light interception of the tree. EduAPPLE enables high-quality views of trees and their light interception from all angles in real time and is designed for education regarding free-standing apple tree training (spindle). It can, therefore, be used in schools, universities, and other educational organizations, as well as by tree growers, including the large number of growers having only a few fruit trees.
L-PEACH is an L-system-based functional-structural model for simulating architectural growth and carbohydrate partitioning among individual organs in peach (Prunus persica (L.) Batsch) trees. The ...original model provided a prototype for how tree architecture and carbon economy could be integrated, but did not simulate peach tree architecture realistically. Moreover, evaluation of the functional characteristics of the individual organs and the whole tree remained a largely open issue. In the present study, we incorporated Markovian models into L-PEACH to improve the architecture of the simulated trees. The model was also calibrated to grams of carbohydrate, and tools for systematically displaying quantitative outputs and evaluating the behaviour of the model were developed. The use of the Markovian model concept to model tree architecture in L-PEACH reproduced tree behaviour and responses to management practices visually similar to trees in commercial orchards. The new architectural model along with several improvements in the carbohydrate-partitioning algorithms derived from the model evaluation significantly improved the results related to carbon allocation, such as organ growth, carbohydrate assimilation, reserve dynamics and maintenance respiration. The model results are now consistent within the modelled tree structure and are in general agreement with observations of peach trees growing under field conditions.
•We developed a red spruce habitat model (ARIM.HAB) in the Great Smoky Mountains.•ARIM.HAB was coupled with a temporal tree growth simulation model (ARIM.SIM).•ARIM.HAB well projected the range and ...habitat suitability of red spruce.•ARIM.HAB explained habitat-specific mechanisms underlying projections.•The coupling approach improved accuracy in projecting habitat suitability.
Red spruce (Picea rubens Sargent) has exhibited widespread growth decline and high mortality for the last half century in the eastern United States. Good prediction of this species’ distribution in relation to environmental conditions is critical for effective management. This study projects red spruce distribution in response to multiple causal mechanisms in the Great Smoky Mountains National Park (GSMNP) of the Southern Appalachian Mountains by coupling a temporal simulation model of tree growth (ARIM.SIM) to a species distribution model (ARIM.HAB). ARIM.HAB computed habitat suitability, estimated from ARIM.SIM-generated red spruce growth, for every spatial 30m grid cell in GSMNP. ARIM.SIM showed that different factors were responsible for habitat suitability and growth at higher vs. lower elevations. The air pollution variables (acid rain and cloud immersion frequency) caused low habitat suitability at higher elevations (1800–2028m). Reduced air pollution but greater stress from climatic variables (high temperatures, reduced precipitation) caused medium suitability at lower elevations (1400–1600m). And less stress from air pollution and climate variables combined with ample water to produce highest suitability at intermediate elevations (1600–1800m). The projected range was verified with an existing geospatial database for red spruce and showed excellent correspondence with present-day distribution (AUC=0.99, kappa=0.87 and TSS=0.88). This research shows that species distribution models coupled with a process-based temporal simulation models can improve the precision and accuracy of, respectively, habitat suitability and range projections for species at local scales.
Compte tenu du problème de la faible capacité de recherche globale du robot et de la sécurité du mouvement dans la planification du trajet du robot, en particulier du problème de la façon de ...controuner doucement l'obstacle lorsque celui-ci est rencontré dans le processus d'optimisation du chemin de parcours global, cet article présente un mécanisme de lissage de trajectoire de robot basé sur l'algorithme TGSA (Tree Growth Simulation) et la courbe de Bézierà trois ordres. Dans la modélisation de l'environnement, la carte en grille en nid d'abeille (carte en grille Honeycomb, HGM) est appliquée pour diviser la carte de l’environnement en grille hexagonale avec la même taille, de sorte que l'angle de pas devienne plus petit lorsque le robot contourne l'obstacle, ce qui réduit le problème de collision causé par l'inertie dans une certaine mesure. En termes de recherche de chemin, basée sur le principe du phototropisme des plantes, l’algorithme de simulation de croissance d’arbre a été adopté pour effectuer une optimisation globale du chemin de traversée, qui peut contourner efficacement l’obstacle et trouver le chemin optimal. Dans le lissage de la trajectoire, la courbe de Bézier du troisième ordre est appliquée pour lisser le trajet. Le mécanisme de sélection de trajectoire est établi en fonction de la présence d'obstacle sur la trajectoire lissée et permet d'obtenir une trajectoire optimale. Enfin, la sécurité et le lissage du mouvement du robot sont également vérifiées par une expérience de simulation. In view of the problem of robot’s weak global search ability and motion safety in robot path planning, this paper puts forward a robot path smoothing mechanism based on the Tree Growth Simulation Algorithm (TGSA) and Third-order Bezier Curve. In the environment modeling, the honeycomb grid map (Honeycomb Grid Map, HGM) is applied to divide the environment map into the hexagonal grid with the same size, so that the step angle becomes smaller when the robot bypasses the obstacle, which alleviates the collision problem caused by inertia to some degree. In terms of path search, based on the principle of phototropism of plants, the tree growth simulation algorithm was adopted to carry out global traversal path optimization, which can effectively bypass the obstacle and find out the optimal path. In the path smoothing, the Third-order Bezier curve is applied to smooth the path. The path selection mechanism is established based on whether there is obstacle on the smoothed path , and an optimal smooth path is obtained. The final comparison experiments verify that the safety and smoothness of the robot in this method are improved.
As trees growth simulation algorithm(TGSA) has the disadvantage of bad convergence stability and it is difficult to find the global optimal solution in solving large scale reactive power optimization ...problem, a new reactive power optimization algorithm called chaotic trees growth simulation algorithm(CTGSA) is presented by using the characters that chaos optimization algorithm is sensitive to initial value and has ergodicity. An operator called chaos immigrant is introduced to the process of TGSA to improve the quality of feasible solutions in the growing point collection and keep the diversity of feasible solutions, by which the new algorithm has better convergence stability and optimization precision. When the algorithm is used for IEEE 30-bus system, the results show that the algorithm has strongly global optimization ability and convergence stability.
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