Tree Structure Research Articles

Adaptive Merkle trees for enhanced blockchain scalability

The scalability of blockchain technology remains a critical challenge, hindering its widespread adoption across various sectors. This study introduces an innovative approach to address this challenge by proposing the adaptive restructuring of Merkle trees, fundamental components of blockchain architecture responsible for ensuring data integrity and facilitating efficient verification processes. Unlike traditional static tree structures, our adaptive model dynamically adjusts the configuration of these trees based on usage patterns, significantly reducing the average path length required for verification and, consequently, the computational overhead associated with these processes. Through a comprehensive conceptual framework, we delineate the methodology for adaptive restructuring, encompassing both binary and non-binary tree configurations. This framework is validated through a series of detailed examples, demonstrating the practical feasibility and the efficiency gains achievable with our approach. To empirically assess the effectiveness of our proposed method, we conducted rigorous experiments using real-world data from the Ethereum blockchain. The results provide compelling evidence for the superiority of adaptive Merkle trees, with efficiency gains of up to 30% and higher observed during the initial stages of tree restructuring. Moreover, we present a comparative analysis with existing scalability solutions, highlighting the unique advantages of adaptive restructuring in terms of simplicity, security, and efficiency enhancement without introducing additional complexities or dependencies. This study’s implications extend beyond theoretical advancements, offering a scalable, secure, and efficient method for blockchain data verification that could facilitate broader adoption of blockchain technology in finance, supply chain management, and beyond. As the blockchain ecosystem continues to evolve, the principles, methodologies, and empirical findings outlined herein are poised to contribute significantly to its growth and maturity. Statement The findings of this article were presented at ETHDenver 2024 (https://www.ethdenver.com/), a leading innovation festival that champions the Web3 community’s role in shaping the future of blockchain technology. A video of our presentation can be found at https://www.youtube.com/watch?v=-jjYVCAQkNE. The original manuscript of this article is available on the preprint archive at https://arxiv.org/abs/2403.00406.

Intensifying aridity induces tradeoffs among biodiversity and ecosystem services supported by trees

AbstractAimChanges in climate are likely to have major impacts on benefits (i.e., biodiversity and ecosystem services) supported by trees. Here we explore the extent to which trees can support multiple benefits, and the potential tradeoffs among them, under increasing dryness.LocationEastern Australia.Time period2018–2019.Major taxa studiedTrees.MethodsWe evaluated changes in biodiversity and services supported by trees and the nature of potential tradeoffs in response to increasing aridity, our proxy of drying regional climates. We assessed six benefits (biodiversity and five ecosystem services) supported by trees at 126 sites across a gradient from Australia's mesic coast to the arid interior.ResultsThe value of average benefits did not vary with aridity, with winners and losers in biodiversity and ecosystem services as aridity intensified. Tradeoffs between biodiversity and soil stability declined with increasing aridity, but only in mesic environments, whereas tradeoffs between wood production potential and carbon storage intensified under greater aridity levels, but only in mesic environments. Aridity and tree structure were the major regulators of these tradeoffs, particularly under dry environments. Increasing aridity affected tradeoffs directly or indirectly by either suppressing the positive effect of tree height or exacerbating the negative effect of tree canopy size.Main conclusionsOur results indicate that biodiversity and most ecosystem services supported by trees are likely to decline under future climate change scenarios and demonstrate the importance of targeting afforestation programs to specific services in particular climatic areas rather than attempting to improve multiple services.

Efficient solar-driven crude oil cleanup via graphene/cellulose aerogel with radial and centrosymmetric design

Frequent oil spills pose significant threats to ecosystems; therefore, strict requirements are needed for prompt remediation and reclamation of spilled oil. Influenced by the structure of coniferous trees and their water transport, this experiment used cellulose nanofiber (CNF), polyvinyl alcohol (PVA), and methyltrimethoxysilane (MTMS) to prepare radially centrosymmetric aerogels. By utilizing the in-situ polycondensation reaction of MTMS, CNF, and PVA were connected, and the hydrophobicity and mechanical properties of the aerogel were greatly enhanced. Furthermore, the introduction of graphene oxide (GO), enshrouded within the cross-linked network, engenders heightened photo-thermal effects. The resultant composite aerogel exhibits expeditious oil absorption under solar irradiation and radial layered channel architecture, significantly curtailing the crude oil absorption timeframe (achieving a maximum absorption capacity of 51.7 g/g). Moreover, it demonstrates superior performance in rapidly and repeatedly adsorbing highly viscous crude oil, surpassing existing literature. Notably, continuous absorption of high-viscosity crude oil is achieved by integrating the composite aerogel with a peristaltic pump. This study offers a novel approach to the absorption and retrieval of high-viscosity crude oil, broadening the potential application horizons of CNF-based aerogels within environmental remediation.

DeepTree: Modeling Trees With Situated Latents.

In this article, we propose DeepTree, a novel method for modeling trees based on learning developmental rules for branching structures instead of manually defining them. We call our deep neural model "situated latent" because its behavior is determined by the intrinsic state -encoded as a latent space of a deep neural model- and by the extrinsic (environmental) data that is "situated" as the location in the 3D space and on the tree structure. We use a neural network pipeline to train a situated latent space that allows us to locally predict branch growth only based on a single node in the branch graph of a tree model. We use this representation to progressively develop new branch nodes, thereby mimicking the growth process of trees. Starting from a root node, a tree is generated by iteratively querying the neural network on the newly added nodes resulting in the branching structure of the whole tree. Our method enables generating a wide variety of tree shapes without the need to define intricate parameters that control their growth and behavior. Furthermore, we show that the situated latents can also be used to encode the environmental response of tree models, e.g., when trees grow next to obstacles. We validate the effectiveness of our method by measuring the similarity of our tree models and by procedurally generated ones based on a number of established metrics for tree form.

Self-organizing hierarchical incremental learning framework and universal approximation analysis based on stochastic configuration mechanism

Conventional machine learning algorithms face significant limitations when dealing with high-dimensional data. Besides, deep learning models often require substantial computational resources and have a high processing time despite their excellent performance. Hence, this paper proposes an expanded stochastic configuration network and a self-organizing hierarchical incremental learning (SHIL) framework to overcome these challenges. Specifically, this study introduces a novel supervised hierarchical clustering tree based on the minimum redundancy maximum correlation algorithm, which mines internal data structures to construct diverse hierarchies. Subsequently, by exploiting the parent-child node relationships in the tree structure, SHIL defines the maximum number of nodes as the switching condition between levels uses the supervisory mechanism as the parameter selection criterion, and adopts the tolerance error as the termination criterion for the training. Furthermore, the universal approximation property of the SHIL framework is provided. The proposed SHIL framework is validated on several benchmark datasets, image datasets, and industrial robot cases, with the corresponding experimental results demonstrating that SHIL significantly improves computational efficiency and ensures high accuracy.

Multi-view active sensing for human–robot interaction via hierarchically connected tree

Comprehensive perception of human beings is the prerequisite to ensure the safety of human–robot interaction. Currently, visual sensing approaches typically involve a single static camera, resulting in a restricted and occluded field of view. In our work, we develop an active vision system using multiple cameras to dynamically capture multi-source RGB-D data. An integrated human sensing strategy based on a hierarchically connected tree structure is proposed to fuse localized visual information. Constituting the tree model are the nodes representing keypoints and the edges representing keyparts, which are consistently interconnected to preserve the structural constraints during multi-source fusion. Utilizing RGB-D data and HRNet, the 3D positions of keypoints are analytically estimated, and their presence is inferred through a sliding widow of confidence scores. Subsequently, the point clouds of reliable keyparts are extracted by drawing occlusion-resistant masks, enabling fine registration between data clouds and cylindrical model following the hierarchical order. Experimental results demonstrate that our method enhances keypart recognition recall from 69.20% to 90.10%, compared to employing a single static camera. Furthermore, in overcoming challenges related to localized and occluded perception, the robotic arm’s obstacle avoidance capabilities are effectively improved.

Inferring dynamical models from time-series biological data using an interpretable machine learning method based on weighted expression trees

The growing time-series data make it possible to glimpse the hidden dynamics in various fields. However, developing a computational toolbox with high interpretability to unveil the interaction dynamics from data remains a crucial challenge. Here, we propose a new computational approach called automated dynamical model inference based on expression trees (ADMIET), in which the machine learning algorithm, the numerical integration of ordinary differential equations and the interpretability from prior knowledge are embedded into the symbolic learning scheme to establish a general framework for revealing the hidden dynamics in time-series data. ADMIET takes full advantage of both machine learning algorithm and expression tree. Firstly, we translate the prior knowledge into constraints on the structure of expression tree, reducing the search space and enhancing the interpretability. Secondly, we utilize the proposed adaptive penalty function to ensure the convergence of gradient descent algorithm and the selection of the symbols. Compared to gene expression programming, ADMIET exhibits its remarkable capability in function fitting with higher accuracy and broader applicability. Moreover, ADMIET can better fit parameters in nonlinear forms compared to regression methods. Furthermore, we apply ADMIET to two typical biological systems and one real data with different prior knowledge to infer the dynamical equations. The results indicate that ADMIET can not only discover the interaction relationships but also provide accurate estimates of the parameters in the equations. These results demonstrate ADMIET’s superiority in revealing interpretable dynamics from time-series biological data.

A permutable MLP-like architecture for disease prediction from gut metagenomic data

Metagenomic data plays a crucial role in analyzing the relationship between microbes and diseases. However, the limited number of samples, high dimensionality, and sparsity of metagenomic data pose significant challenges for the application of deep learning in data classification and prediction. Previous studies have shown that utilizing the phylogenetic tree structure to transform metagenomic abundance data into a 2D matrix input for convolutional neural networks (CNNs) improves classification performance. Inspired by the success of a Permutable MLP-like architecture in visual recognition, we propose Metagenomic Permutator (MetaP), which applied the Permutable MLP-like network structure to capture the phylogenetic information of microbes within the 2D matrix formed by phylogenetic tree. Our experiments demonstrate that our model achieved competitive performance compared to other deep neural networks and traditional machine learning, and has good prospects for multi-classification and large sample sizes. Furthermore, we utilize the SHAP (SHapley Additive exPlanations) method to interpret our model predictions, identifying the microbial features that are associated with diseases.

BCDA: A blockchain-based dynamic auditing scheme for intelligent IoT

Intelligent Internet of Things (IoT) provides services for machine learning applications by collecting real-time data, where data correctness crucially impacts the training accuracy of models. Data owners host incremental real-time data in the cloud and use blockchain as a public platform for data query, usage, and certification. Malicious attackers may manipulate data through methods like data poisoning, compromising model parameters. Blockchain-based data integrity verification (auditing) schemes can effectively detect and monitor such activities. However, managing the storage demands for massive on-chain data certificates poses significant challenges. Existing compressed certificate storage strategies, such as the Merkle-Hash tree (MHT), incur significant on-chain storage costs because the auxiliary paths and the entire tree structure must be uploaded during verification, resulting in an O(n) storage overhead. Polynomial commitments offer a promising alternative by reducing storage costs to hundreds of bytes. However, current polynomial commitment methods lack effective support for incremental data updates, which are essential for real-time applications. In this paper, we propose a novel polynomial commitment-based dynamic integrity verification scheme that implements cumulative commitment updates for the first time. Our scheme includes a new data block insertion and deletion strategy that maintains the original order of data blocks within polynomial commitments. Theoretical security analysis confirms the robustness of the proposed scheme, while simulation results demonstrate that it meets the efficiency requirements for blockchain-based distributed data integrity verification mechanisms.

A high speed pipelined radix-16 Booth multiplier architecture for FPGA implementation

Since multiplication is a complex and resource-consuming operation, it is very effective on the speed performance of a processor. In this regard, fast multiplication unit design is important in digital system architectures. FPGA hardware, which is efficient for the implementation and rapid prototyping of today’s digital system architectures, is becoming widespread. Therefore, in this study it aimed to design a fast radix-16 Booth multiplier based on the FPGA architecture. Booth multiplier implementation is preferred because it is relatively simple and efficient. The sizes of the multiplexers, which have an impact on the operating period in the encoder part of the proposed multiplier, reduced in the designed architecture by using a simple algorithm. To increase the speed performance of the system, the pipeline technique is used and parallelization is preferred in the tree structure in addition of the partial products. The effects of different multiplexer sizes, bit lengths and pipeline stages on the operating period of the system and other performance metrics examined. Different designs and the results obtained presented. According to the results, the multiplier hardware architectures designed in this study exhibit effective results in terms of speed performance.

Comparative analysis of data-driven models and signal processing techniques in the monthly maximum daily precipitation prediction of El Kerma station Northeast of Algeria

The availability of a climate database is an essential requirement for modeling and mapping hydrological and environmental processes. Regardless of the nature and structure of these models, most of them require a complete and reliable dataset on a spatiotemporal basis. Unfortunately, the measurement of hydrological variables (precipitation, flow, etc.) can be affected by systematic errors, gaps and random data. The watershed of Seybouse located in the North-East of Algeria, has a network that has gaps in the monthly data with an average error percentage of 20.46% over the period of 1970–2008 on all the stations in operation. The current study combines support vector regression, artificial neural networks, boosted tree, bagged tree, gaussian processes regression and linear regression techniques with empirical mode decomposition and discrete wavelet transform techniques to fill the missing precipitation gaps. This study aims to determine which kernel function, regression type, tree and network structure and data decomposition technique will produce the best outputs in predicting missing rainfall. For this purpose, it aims to comprehensively evaluate various soft computing and signal processing models and use strengthened hybrid approaches by combining them. While modeling, 70% of the data was used for training and the rest for testing. Mean absolute error (MAE), root mean square error (RMSE), determination coefficient (R2), Taylor diagram and Violin plots were used to find the best model. As a result of the analysis, it was concluded that the SVM model produced superior outputs compared to other machine learning models. The single LSVM model had an R2 value of 0.78, RMSE of 8.18, and MAE of 5.90. The W-LSVM model had an R2 value of 0.78, RMSE of 8.10, and MAE of 5.90. The EMD-CGSVM model had an R2 value of 0.69, RMSE of 10.43, and MAE of 7.10. These results indicate that the W-LSVM models provided the most accurate rainfall forecast outputs. The results showed that the wavelet-based linear support vector regression model in precipitation prediction is the most successful artificial intelligence model. Finally, using the W-LSVM model to predict rainfall in the study area can significantly improve water resources management and flood strategies and help optimize water use and structures.

An Efficient System for Large-Scale Collaborative Tasks in Government

Government collaboration tasks are integral for grassroots governance and essential for government administration. Large-scale government collaboration tasks often involve multiple departments working together to solve complex tasks that require handling large amounts of data. However, existing offline processes make it difficult to manage complex collaboration scenarios and obtain timely execution results. We introduce the concept of a spatio-temporal task tree and propose a log-based incremental update method for updating statistical values in the tree structure to fulfill the need for real-time aggregation analysis of cross-temporal task execution situations. The spatio-temporal task tree fosters collaboration across various departments and organizational hierarchies to execute complex and large-scale tasks in government effectively. By flexible adaptations to different business models, this framework addresses the shortcomings of conventional systems in promoting inter-organizational collaboration. The log-based incremental update method for calculating statistical values within a tree structure allows for the updating of multi-dimensional data statistics in seconds. This approach effectively decreases computational expenses, enhances query efficiency, and improves real-time computation response. Results from experiments demonstrate the feasibility and high availability of the proposed approach, showcasing their potential to address the challenges associated with large-scale collaborative tasks in government affairs.

Interactive Invigoration: Volumetric Modeling of Trees with Strands

Generating realistic models of trees and plants is a complex problem because of the vast variety of shapes trees can form. Procedural modeling algorithms are popular for defining branching structures and steadily increasing their expressive power by considering more biological findings. Most existing methods focus on defining the branching structure of trees based on skeletal graphs, while the surface mesh of branches is most commonly defined as simple cylinders. One critical open problem is defining and controlling the complex details observed in real trees. This paper aims to advance tree modeling by proposing a strand-based volumetric representation for tree models. Strands are fixed-size volumetric pipes that define the branching structure. By leveraging strands, our approach captures the lateral development of trees. We combine the strands with a novel branch development formulation that allows us to locally inject vigor and reshape the tree model. Moreover, we define a set of editing operators for tree primary and lateral development that enables users to interactively generate complex tree models with unprecedented detail with minimal effort.

A simple method to prepare and characterize ZnO/V2O5 nanostructure thin films for photoelectrochemical and photocatalytic uses in visible light

In this research, we investigated the enhancement of the photocatalytic efficiency of ZnO nanorods by incorporating vanadium pentoxide (V2O5). Zinc oxide (ZnO) nanorods were initially prepared by the chemical bath deposition (CBD) method. Subsequently, vanadium pentoxide (V2O5) nanoparticles, produced by laser ablation, were deposited onto the ZnO nanorods via drop casting. Scanning electron microscopy (SEM) images verified that ZnO grew in the morphology of nanorods and nanotubes, while V2O5 exhibited the structure of tree leaves and nanoparticles. The XRD technique was used to investigate the crystalline structure of the produced ZnO/ V2O5 nanostructure. The high band gap in ZnO limits the efficiency of photocatalysis under visible light. Building a core–shell structure with materials such as V2O5 can boost their performance in such conditions. The prepared samples appeared photodegradation rate of the MB dye reached 39% after 9 h of exposure to visible light. The photoelectrochemical cell measurement of the prepared ZnO/ V2O5 nanostructures demonstrated a positive response to light and achieved a relatively high photoconversion efficiency of 0.084% at 0.35 V, surpassing the results of earlier investigations. The M-S analysis revealed that the ZnO/ V2O5 nanostructure thin films exhibited n-type conductivity, characterized by a negative flat band potential VFB.

Mining Weighted Patterns from Time Series Databases Based on Sliding Window

Data mining has traditionally relied heavily on sliding window-based challenges, which has sparked a variety of studies. For each new window in time series mining, current literature mandates the rebuilding of the underlying structure, Suffix Tree - A trie-based structure representing all the suffixes of a string. However, reconstruction struggles when the window is wide or when sliding happens frequently. As a result, we provide a new technique Dynamic Tree-Based Approach to handle Sliding Windows (DTSW) in time series in this study that dynamically changes the representative suffix tree structure rather than reconstructing it after every alteration or sliding. In addition, we also put forth a different approach to the issue of extracting weighted periodic patterns from time series. To prevent testing pointless patterns, existing studies mostly rely on the weight of the database's highest-weighted item. However, these methods continue to examine numerous patterns. These methods still examine numerous patterns to see whether they can be candidates. Our proposed measure Maximum Possible Weighted Support (MPWS) accelerates the candidate generation process by removing numerous unnecessary patterns in advance. The novelty of MPWS is it considers the maximum weighted average over the maximum weighted item extension by enforcing more constraints. The usefulness of our two techniques in handling sliding windows and trimming redundant candidate patterns is demonstrated by experimental results using a variety of real-world datasets. Our experiments state that our dynamic handling technique significantly improves runtime than the reconstruction in a dynamic sliding window-based environment with simultaneous insertion and deletion actions and MPWS reduces the number of tested patterns resulting in lesser mining time in weighted time series pattern mining. DUJASE Vol. 8 (1) 13-25, 2023 (January)

Tree species diversity drives the land surface phenology of seasonally dry tropical woodlands

Abstract Seasonal foliage display (leaf phenology) is a key determinant of ecosystem function. Variation in land surface phenology, observed via space‐borne remote sensing, can be explained at broad spatial scales by climate, but we lack understanding of how vegetation structure and floristic diversity mediates these relationships. This lack of understanding hampers our ability to predict changes in phenology and therefore ecosystem function, in light of rapid ongoing shifts in biodiversity and ecosystem structure due to land use and climate change. We combined a network of 619 vegetation monitoring sites across seasonally dry tropical deciduous woodlands in Zambia with land surface phenology metrics to investigate the role of tree species diversity, composition and vegetation structure on patterns of land surface phenology, including the phenomenon of pre‐rain green‐up. Tree species diversity was associated with earlier pre‐rain green‐up, a longer growing season, and greater cumulative foliage production. Independent of diversity, proportional abundance of Detarioideae species (subfamily of Fabaceae) was associated with a longer growing season by facilitating earlier pre‐rain green‐up. Woodland stands with larger trees green up earlier, suggesting access to deep groundwater reserves. Senescence metrics showed variation among sites but were not well‐explained by precipitation, temperature, structure or diversity. Synthesis: Tree diversity, composition and structure are co‐determinants of seasonal patterns of foliage display in seasonally dry tropical deciduous woodlands, as measured via land surface phenology, at regional scale. Our study identifies both a niche complementarity effect whereby diverse woodlands exhibit longer growing seasons, as well as a mass‐ratio effect whereby detarioid species drive earlier pre‐rain green‐up, providing insights into the mechanisms underlying the biodiversity ecosystem function relationship in this biome. We stress the importance of considering biotic controls on ecosystem functioning in the next generation of earth‐system models predicting the response of communities to global change.

On suffix tree detection

A suffix tree is a fundamental data structure for string processing and information retrieval, however, its structure is still not well understood. The suffix trees reverse engineering problem, which its research aims at reducing this gap, is the following. Given an ordered rooted tree T with unlabeled edges, determine whether there exists a string w such that the unlabeled-edges suffix tree of w is isomorphic to T. Previous studies on this problem consider the relaxation of having the suffix links as well as assume a binary alphabet. This paper is the first to consider the suffix tree detection problem, in which the relaxation of having suffix links as input is removed. We study suffix tree detection on two scenarios that are interesting per se. We provide a suffix tree detection algorithm for general alphabet periodic strings. Given an ordered tree T with n leaves, our detection algorithm takes O(n+|Σ|p)-time, where p is the unknown in advance length of a period that repeats at least 3 times in a string S having a suffix tree structure identical to T, if such S exists. Therefore, it is a polynomial time algorithm if p is a constant and a linear time algorithm if, in addition, the alphabet has a sub-linear size. We also show some necessary (but insufficient) conditions for binary alphabet general strings suffix tree detection. By this we take another step towards understanding suffix trees structure.

Feature-Enhanced Multi-Task Learning for Speech Emotion Recognition Using Decision Trees and LSTM

Speech emotion recognition (SER) plays an important role in human-computer interaction (HCI) technology and has a wide range of application scenarios in medical medicine, psychotherapy, and other applications. In recent years, with the development of deep learning, many researchers have combined feature extraction technology with deep learning technology to extract more discriminative emotional information. However, a single speech emotion classification task makes it difficult to effectively utilize feature information, resulting in feature redundancy. Therefore, this paper uses speech feature enhancement (SFE) as an auxiliary task to provide additional information for the SER task. This paper combines Long Short-Term Memory Networks (LSTM) with soft decision trees and proposes a multi-task learning framework based on a decision tree structure. Specifically, it trains the LSTM network by computing the distances of features at different leaf nodes in the soft decision tree, thereby achieving enhanced speech feature representation. The results show that the algorithm achieves 85.6% accuracy on the EMO-DB dataset and 81.3% accuracy on the CASIA dataset. This represents an improvement of 11.8% over the baseline on the EMO-DB dataset and 14.9% on the CASIA dataset, proving the effectiveness of the method. Additionally, we conducted cross-database experiments, real-time performance analysis, and noise environment analysis to validate the robustness and practicality of our method. The additional analyses further demonstrate that our approach performs reliably across different databases, maintains real-time processing capabilities, and is robust to noisy environments.

Assembly processes and co-occurrence of bacterial communities in tree rhizosphere under Pb-Zn contamination

Rhizosphere bacteria are critical for supporting plant performance in stressful environments. Understanding the assembly and co-occurrence of rhizosphere bacterial communities contributes significantly to both plant growth and heavy metal accumulation. In this study, Ligustrum lucidum and Melia azedarach were planted in soils with simulated varying levels of Pb-Zn contamination. The Rhizosphere bacterial communities were investigated by using 16S rRNA gene sequencing. The impacts of Pb-Zn contamination on the diversity and structure of the rhizosphere bacterial community were found to be greater than those of both tree species. The variation in bacterial community structure in both trees was mainly driven by the combinations of Pb-Zn and soil properties. Deterministic processes (non-planted, 82 %; L. lucidum, 73 %; M. azedarach, 55 %) proved to be the most important assembly processes for soil bacterial communities, but both trees increased the importance of stochastic processes (18 %, 27 %, 45 %). The rhizosphere co-occurrence networks exhibited greater stability compared to the non-planted soil networks. Rare taxa played a dominant role in maintaining the stability of rhizosphere networks, as most of the keystone taxa within rhizosphere networks belonged to rare taxa. Dissimilarities in the structure and network complexity of rhizosphere bacterial communities were significantly associated with differences in tree biomass and metal accumulation. These variations in response varied between both trees, with L. lucidum exhibiting greater potential for phytoremediation in its rhizosphere compared to M. azedarach. Our results offer valuable insights for designing effective microbe-assisted phytoremediation systems.

Tree Species Composition and Structure of a Vegetation Plot in a Montane Forest in the Department of Amazonas, Peru

The diversity and floristic composition of a primeval forest was studied, located in the district of Yambrasbamba–Bongará–Amazonas, delimiting a 1 ha area, and at an altitude of 1890 m.a.s.l. All individuals with diameter at breast height (DBH) ≥ 10 cm were inventoried. The plant diversity in the area was measured and a description of its composition and floristic structure was made. The following were recorded: a total of 640 trees distributed in 39 families, 60 genera and 152 species. The value of the Simpson’s index (D) was 0.974 and that of the Shannon–Wiener index was 4.264, indicating that the species had a high abundance of individuals. In turn, Fisher’s alpha value (α) was 23.744, indicating a regular diversity in montane forests in relation to different altitudinal gradients. The families with the highest number of individuals were Melastomataceae, Rubiaceae, Euphorbiaceae, Phyllanthaceae, and Lauraceae. The most abundant species were Alchornea acutifolia Müll.Arg. with 47 individuals (7.34%), Chimarrhis glabriflora Ducke with 39 individuals (6.09%), Hieronyma alchorneoides Allemão with 39 individuals (6.09%), and Cyathea lasiosora (Kuhn) Domin with 33 individuals (5.16%). A comparative analysis was carried out of plots of montane and premontane forests, and the studied plot presented had the third-highest register of families and genera, behind the plots studied in the provinces of Oxapampa and Chanchamayo.