Academic literature on the topic 'Tress gradient hypothesis'

AbstractUnderstanding tree and stand growth dynamics in the frame of climate change calls for large-scale analyses. For analysing growth patterns in mountain forests across Europe, the CLIMO consortium compiled a network of observational plots across European mountain regions. Here, we describe the design and efficacy of this network of plots in monospecific European beech and mixed-species stands of Norway spruce, European beech, and silver fir.First, we sketch the state of the art of existing monitoring and observational approaches for assessing the growth of mountain forests. Second, we introduce the design, measurement protocols, as well as site and stand characteristics, and we stress the innovation of the newly compiled network. Third, we give an overview of the growth and yield data at stand and tree level, sketch the growth characteristics along elevation gradients, and introduce the methods of statistical evaluation. Fourth, we report additional measurements of soil, genetic resources, and climate smartness indicators and criteria, which were available for statistical evaluation and testing hypotheses. Fifth, we present the ESFONET (European Smart Forest Network) approach of data and knowledge dissemination. The discussion is focussed on the novelty and relevance of the database, its potential for monitoring, understanding and management of mountain forests toward climate smartness, and the requirements for future assessments and inventories.In this chapter, we describe the design and efficacy of this network of plots in monospecific European beech and mixed-species stands of Norway spruce, European beech, and silver fir. We present how to acquire and evaluate data from individual trees and the whole stand to quantify and understand the growth of mountain forests in Europe under climate change. It will provide concepts, models, and practical hints for analogous trans-geographic projects that may be based on the existing and newly recorded data on forests.

Abstract Among the 79 taxa of Microchiroptera in Australasia, frequency of treecavity roosting increases as mean annual temperature decreases and latitude increases. This gradient suggests there may be significant thermal benefits to tree-cavity roosting in cold climates. We explore the causes and consequences of tree-cavity roosting during summer months in Chalinolobus tuberculatus, a species that occurs at the southern limit (highest latitude) of this gradient. Five geographically distinct populations are compared. C. tuberculatus selected the oldest and largest trees for maternity roosting and avoided roosting under bark and in caves and buildings, despite the abundance of these sites. It also selected small, well-insulated cavities that accrue significant energy conservation benefits compared with other potential roosts (the ‘‘thermal hypothesis’’). Reproductive females selected roosts that reach maximum temperatures late in the day and retain high temperatures through the night, thus benefiting nonvolant young. Productivity and survival were significantly higher in populations that selected well-insulated roosts. We propose that selection favors smaller, rather than larger, roosting group sizes in this cold, temperate climate. Smaller groups of bats that use relatively small, well-insulated cavities have higher survival rates than larger groups that use larger, less insulated cavities. C. tuberculatus formed behaviorally, though not geographically, isolated subgroups. All colonies exhibited extreme roost-site lability on a daily basis, but strong long-term philopatry among pools exceeding 100 roosts. Most roosts were used once per year but date of reuse was similar each year. Strict temporal philopatry suggests that bats do not switch roosts in response to daily variability in weather conditions. The thermal hypothesis suggests that development of grouping behavior may be an incidental response to physiological constraints on thermoregulation and reproduction. Nevertheless, social interdependence would increase the probability that clusters are large enough on any one day to be thermally beneficial and individuals could improve the reproductive success of other relatives within the group. We conclude by outlining hypotheses that could test the general applicability of findings to tree-cavity roosting bats.

We propose to develop a Deep Learning (DL) framework based on the paradigm of Genetic Programming (GP). The hypothesis is that GP non-parametric and non-differentiable learning units (abstract syntax trees) have the same learning and representation capacity to Artificial Neural Networks (ANN). In an analogy to the traditional ANN/Gradient Descend/Backpropagation DL approach, the proposed framework aims at building a DL alike model fully based on GP. Preliminary results when approaching a number of application domains, suggest that GP is able to deal with large amounts of training data, such as

Abstract The objective of the study is to build a robust Recurrent Neural Network system using Long-Short-Term-Memory (LSTM) to predict future vibrations during drilling operations. This provides a reliable solution to the complex problem of modeling several forms of vibrations encountered downhole. This accurate prediction system can be readily integrated into advisory/warning systems giving drillers the potential to save time, improve safety, and increase efficiency in drilling operations. High-frequency downhole drilling data onshore fields, obtained from a major O&G service provider, w

Abstract Understanding downhole dysfunction, such as stick-slip, is critical for preventing downhole tool damage and reducing non-productive time during drilling. Common methods for quantifying dysfunction intensity, such as a surface Stick-Slip Index (SSI), often fail to capture the complex relationships between surface measurements and downhole behavior. This can lead to improper evaluation of actual downhole conditions and driller response. This paper presents a machine learning model for real-time downhole rotations per minute (RPM) range prediction with surface data which is coupled with