This research aimed to develop statistical models to predict basal area increment (BAI) for Araucaria angustifolia using Artificial Neural Networks (ANNs). Tree species were measured for their biometric variables and identified at the species level. The data were subdivided into three groups: (1) intraspecific competition with A. angustifolia; (2) the first group of species that causes interspecific competition with A. angustifolia; and (3) the second group of species that causes interspecific competition with A. angustifolia. We calculated both the dependent and independent distance and the described competition indices, considering the impact of group stratification. Multi-layer Perceptron (MLP) ANN was structured for modeling. The main results were that: (i) the input variables size and competition were the most significant, allowing us to explain up to 77% of the A. angustifolia BAI variations; (ii) the spatialization of the competing trees contributed significantly to the representation of the competitive status; (iii) the separate variables for each competition group improved the performance of the models; and (iv) besides the intraspecific competition, the interspecific competition also proved to be important to consider. The ANN developed showed precision and generalization, suggesting it could describe the increment of a species common in native forests in Southern Brazil and with potential for upcoming forest management initiatives.

Crown variables are used in growth and production models to increase the accuracy of estimates. These variables are costly and difficult to measure, mainly in dense native forests such as mixed omb...

Artificial intelligence (AI) seeks to simulate the human ability to reason, make decisions, and solve problems. Several AI methodologies have been introduced in forestry to reduce costs and increase accuracy in estimates. We evaluate the performance of Artificial Neural Networks (ANN) in estimating the heights of Araucaria angustifolia (Bertol.) Kuntze (Brazilian pine) trees. The trees are growing in Uneven-aged Mixed Forests (UMF) in southern Brazil and are under different levels of competition. The dataset was divided into training and validation sets. Multi-layer Perceptron (MLP) networks were trained under different Data Normalization (DN) procedures, Neurons in the Hidden Layer (NHL), and Activation Functions (AF). The continuous input variables were diameter at breast height (DBH) and height at the base of the crown (HCB). As a categorical input variable, we consider the sociological position of the trees (dominant–SP1 = 1; codominant–SP2 = 2; and dominated–SP3 = 3), and the continuous output variable was the height (h). In the hidden layer, the number of neurons varied from 3 to 9. Results show that there is no influence of DN in the ANN accuracy. However, the increase in NHL above a certain level caused the model’s over-fitting. In this regard, around 6 neurons stood out, combined with logistic sigmoid AF in the intermediate layer and identity AF in the output layer. Considering the best selected network, the following values of statistical criteria were obtained for the training dataset (R² = 0.84; RMSE = 1.36 m, and MAPE = 6.29) and for the validation dataset (R² = 0.80; RMSE = 1.49 m, and MAPE = 6.53). The possibility of using categorical and numerical variables in the same modeling has been motivating the use of AI techniques in different forestry applications. The ANN presented generalization and consistency regarding biological realism. Therefore, we recommend caution when determining DN, amount of NHL, and using AF during modeling. We argue that such techniques show great potential for forest management procedures and are suggested in other similar environments.

O conceito de biodiversidade está intimamente associado com a variedade genética capaz de diferenciar os indivíduos e suas respectivas populações. Portanto, a diversidade biológica é resultado do longo processo da história evolutiva, que permite a seleção do conjunto de espécies em um dado local. E, esse conjunto de espécies que compõe os ecossistemas fornece serviços fundamentais a existência da humanidade. No entanto, a extensa exploração antrópica promove diversas alterações na dinâmica dos ecossistemas, o que culmina, em muitos casos, na perda da biodiversidade local e consequentemente dos serviços ecossistêmicos. Isso nos impõe um grande desafio na atualidade: frear as pressões que oferecem riscos à biodiversidade global. Para tanto, conhecer essa biodiversidade presente em diferentes escalas (local, regional, global) pode representar o primeiro passo para proposição de medidas que visam a conservação, isto porque, esse mesmo conhecimento possibilita a construção de mapas de distribuição das espécies ao longo de gradientes ambientais, bem como a identificação daquelas que estão sob risco de extinção. Atualmente, há 34 áreas no mundo classificadas como Biodiversity Hotspots, correspondendo a áreas ricas em espécies endêmicas (aquelas que só ocorrem ali). Particularmente, o Brasil apresenta dois ecossistemas que incluem Hotspots (Floresta Atlântica e Cerrado) e ainda estima-se que nosso país possua cerca de 20% de toda a biodiversidade mundial, dados que nos colocam diante de uma problemática: temos estratégias potenciais para caracterização, conservação e manejo sustentável dessa biodiversidade? A obra "Biodiversidade Brasileira: Aspectos do Estado Atual 2" foi elaborada a partir da coletânea de trabalhos científicos, desenvolvidos por pesquisadores de diferentes instituições, e vislumbra contribuir para o aprofundamento do conhecimento acerca da biodiversidade brasileira sob diferentes aspectos. O e-Book contempla 6 capítulos, entre os quais encontramos discussões acerca da ocorrência e dinâmica de espécies em ecossistemas diversificados, como: marinhos, amazônicos e de cerrado. Além desses, um dos trabalhos aborda o emprego de ferramentas digitais na educação ambiental, com enfoque na conservação da biodiversidade. Desta forma, o presente volume é dedicado a discussão da biodiversidade brasileira e representa uma ferramenta de ampliação do conhecimento, sendo voltada a todos aqueles que tenham interesse em conhecer mais sobre a temática, sejam em instituições públicas ou privadas, pesquisadores ou acadêmicos de todos os níveis (graduação e pós-graduação). Desejamos que aqueles que buscam conhecer mais das riquezas biológicas de nosso país, encontrem nessa obra uma alternativa de conhecimento. Magnólia de Araújo Campos Daniele Jovem-Azevêdo

Forest biomass use provides opportunities for implementing strategies to reduce greenhouse gas emissions and global temperature due to its key role in the world carbon cycle. Biomass of forest origin comes from forest plantations exclusively for energy use or residues from the productive sector. The use of wood as an energy source in Brazil is favored by the high forest productivity of its silvicultural sector. Therefore, this study aimed to provide an overview of advances in forest biomass by focusing on bibliometric analysis. This analysis identified the research fronts involving relationships of forest biomass from the genus Eucalyptus and Corymbia in Brazil. Three hundred thirty-nine publications came from the metadata of scientific articles collected in the Web of Science index. After the selection stage, the analysis yielded 116 articles published in 45 journals and 561 authors. There has been an increasing trend in publications over these years, with a peak of publications in 2020. The Brazilian researcher Jose Luiz Stape (14 publications, h-index:12) stood out with the highest number and the most cited on this topic, followed by the Frenchs (Yann Pierre Nouvellon and Guerric Le Maire). Brazilian authors showed great scientific interest in this topic, which has also been attractive internationally. Our findings revealed gaps in specialized themes in this research line. Therefore, researchers can use these insights to direct new academic research by deepening knowledge in this subject.