FLORISTIC AND STRUCTURAL CHARACTERIZATION OF THE MANGROVE FORESTS IN THE ESTUARY OF THE SÃO FRANCISCO RIVER

The objective of this work was characterize the floristic diversity and the phytosociological structure of the mangrove in the São Francisco River estuary in order to contribute to the conservation of this ecosystem. Thirty-four sampling sites were selected, according to ongoing structural mosaic in the region ranging from 100 to 400 m between themselves, in a total useful area of 0.7625 ha. The evaluated parameters were: density of live and dead trunks; living and dead Basal Area; Absolute Frequency, Relative Frequency and Importance Value. Three typical mangrove species of the São Francisco estuary were found such as Avicennia germinans (L.) Stearn, Rhizophora mangle L. and Laguncularia racemosa (L.) Gaertn. F. Gaertn. The forest height ranged from 2.88 to 15.63 m, the DBH from 3.95 to 19.74 cm, live basal area from 4.22 to 47.83 m² ha-1 and dead basal area from 0.50 to 59.63 m² ha-1. The living trunks density ranged from 375.00 to 9100.00 trunks ha-1 and dead trunk density from 100-2800 trunks ha-1. The described results in this study demonstrated that the mangrove forests presents a structural variability that may be associated with environmental characteristics (marine erosion) and anthropogenic factors. The peculiarities of each site emphasize the importance of preventive actions in the ecosystem conservation

Allometric models for estimating the aboveground biomass of the mangrove Rhizophora mangle

The development of species-specific allometric models is critical to the improvement of aboveground biomass estimates, as well as to the estimation of carbon stock and sequestration in mangrove forests. This study developed allometric equations for estimating aboveground biomass of Rhizophora mangle in the mangroves of the estuary of the São Francisco River, in northeastern Brazil. Using a sample of 74 trees, simple linear regression analysis was used to test the dependence of biomass (total and per plant part) on size, considering both transformed (ln) and not-transformed data. Best equations were considered as those with the lowest standard error of estimation (SEE) and highest adjusted coefficient of determination (R2a). The ln-transformed equations showed better results, with R2a near 0.99 in most cases. The equations for reproductive parts presented low R2a values, probably attributed to the seasonal nature of this compartment. "Basal Area2 × Height" showed to be the best predictor, present in most of the best-fitted equations. The models presented here can be considered reliable predictors of the aboveground biomass of R. mangle in the NE-Brazilian mangroves as well as in any site were this widely distributed species present similar architecture to the trees used in the present study.O desenvolvimento de modelos alométricos espécie-específicos é fundamental para a melhoria das estimativas de biomassa aérea, bem como para a estimativa do estoque e sequestro de carbono em florestas de mangue. Este estudo desenvolveu equações alométricas para estimar a biomassa aérea de Rhizophora mangle nos manguezais do estuário do rio São Francisco, nordeste do Brasil. Usando uma amostra de 74 árvores, análises de regressão linear simples foram usadas para testar a dependência da biomassa (total e por parte da planta) do tamanho, considerando dados transformados (Ln) e não transformados. As melhores equações foram aquelas com menor erro padrão da estimativa (SEE) e maior coeficiente de determinação ajustado (R2a). As equações ln-transformadas apresentaram melhores resultados, com R2a próximo a 0,99 na maioria dos casos. As equações para partes reprodutivas apresentaram valores baixos de R2a, o que pode ser atribuído ao caráter sazonal deste compartimento. "Área basal2×Altura" demonstrou ser o melhor preditor, presente na maioria das equações melhor ajustadas. Os modelos aqui apresentados podem ser considerados preditores confiáveis da biomassa aérea de R. mangle no manguezal do Nordeste brasileiro, bem como em qualquer local onde esta espécie de ampla distribuição assemelhe-se à arquitetura das árvores utilizadas no presente estudo

Factors that negatively influence students’ transition from the traditional classroom to emergency remote education (ERT)

Universities worldwide had to adopt Emergency Remote Teaching (ERT) because of the COVID-19 pandemic. This abrupt change forced students used to face-to-face classes to adapt to a new reality. However, this transition is different for each student because of personal realities. For example, the student’s generation, emotional state, and some factors (e.g., tech skills, technological infrastructure, place of study, and perspectives regarding this change) may influence the feelings of optimism and awareness of learning. This work describes a quantitative study conducted before the first ERT academic semester starts with 1011 undergraduate students measuring those factors through questionnaires. In addition, to test whether the measuring factors are consistent with our understanding, the confirmatory factor analysis (CFA) and the statistical reliability analyses were performed. From the results, we identified differences between the participants’ age generations. The mean scores for the Z generation were lower than other generations concerning the measuring factors and feelings. Plus, it was found that students’ emotional states negatively influence their feelings about ERT. Also, the measuring factors influence optimism and awareness of learning. Therefore, we suggest that institutions around the globe should offer innovative distance learning strategies to train the students for this paradigm shift, identify the students’ needs for the Internet and devices, and provide psychologists to aid the student’s emotional state. Thus, helping a better and faster transition and adaptation of students to the change of educational methodology to improve students’ experience in distance education

Allometric models for estimating the aboveground biomass of the mangrove Rhizophora mangle

Abstract The development of species-specific allometric models is critical to the improvement of aboveground biomass estimates, as well as to the estimation of carbon stock and sequestration in mangrove forests. This study developed allometric equations for estimating aboveground biomass of Rhizophora mangle in the mangroves of the estuary of the São Francisco River, in northeastern Brazil. Using a sample of 74 trees, simple linear regression analysis was used to test the dependence of biomass (total and per plant part) on size, considering both transformed (ln) and not-transformed data. Best equations were considered as those with the lowest standard error of estimation (SEE) and highest adjusted coefficient of determination (R2a). The ln-transformed equations showed better results, with R2a near 0.99 in most cases. The equations for reproductive parts presented low R2a values, probably attributed to the seasonal nature of this compartment. "Basal Area2 × Height" showed to be the best predictor, present in most of the best-fitted equations. The models presented here can be considered reliable predictors of the aboveground biomass of R. mangle in the NE-Brazilian mangroves as well as in any site were this widely distributed species present similar architecture to the trees used in the present study

Allometric models for estimating the aboveground biomass of the mangrove Rhizophora mangle

Abstract The development of species-specific allometric models is critical to the improvement of aboveground biomass estimates, as well as to the estimation of carbon stock and sequestration in mangrove forests. This study developed allometric equations for estimating aboveground biomass of Rhizophora mangle in the mangroves of the estuary of the São Francisco River, in northeastern Brazil. Using a sample of 74 trees, simple linear regression analysis was used to test the dependence of biomass (total and per plant part) on size, considering both transformed (ln) and not-transformed data. Best equations were considered as those with the lowest standard error of estimation (SEE) and highest adjusted coefficient of determination (R2a). The ln-transformed equations showed better results, with R2a near 0.99 in most cases. The equations for reproductive parts presented low R2a values, probably attributed to the seasonal nature of this compartment. "Basal Area2 × Height" showed to be the best predictor, present in most of the best-fitted equations. The models presented here can be considered reliable predictors of the aboveground biomass of R. mangle in the NE-Brazilian mangroves as well as in any site were this widely distributed species present similar architecture to the trees used in the present study