Adérito Celestino Cau

Mr. Adérito Celestino Cau

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Research Focus & Metrics

Academic Career Length

2Years (2025-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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13 May 2025 in Forests
Verified
Journal Article
Open access
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Adérito Celestino CauAdérito Celestino Cau
Adérito Celestino Cau
JJJOSÉ ALVES Junqueira Júnior
JOSÉ ALVES Junqueira Júnior
Alejandra B Bustillos VegaAlejandra B Bustillos Vega
Alejandra B Bustillos Vega
SMSeverino José Macôo
Severino José Macôo
ARAndré Ferreira Rodrigues
André Ferreira Rodrigues
Carlos MelloCarlos Mello
Carlos Mello
The Atlantic Forest has undergone deforestation and prolonged droughts, affecting ecosystem services. This study assesses the water balance using hydrological observations from representative tree species within a Montane Semideciduous Seasonal Forest (MF) remnant. Gross precipitation (GP), canopy interception (CI), and effective precipitation (EP = Throughfall + Stemflow) were recorded daily, and soil moisture was measured down to 1.80 m every two days during the dry period of the 2023/2024 hydrological year. Additionally, aboveground biomass (AGB), fresh root biomass (BR), and soil hydrological properties in the soil profile were obtained to support the water balance results. The highest EP values were recorded in Miconia willdenowii, while the lowest were in Xylopia brasiliensis. Root zone water storage exhibited a declining trend, with the highest values in Miconia willdenowii. ET remained low, mainly in April, July, and September, with Miconia willdenowii and Copaifera langsdorffii showing the highest values, and AGB correlated with CI and ET. The dynamic of this ecosystem is apparent in the temporal variations (CVt) of soil moisture, influenced by EP and ET. The greatest variability was recorded in the surface layer (0–20 cm), stabilizing with depth, especially below 120 cm. The Temporal Stability Index (TSI) of soil water storage indicated greater stability in Blepharocalyx salicifolius. This study highlights the significance of soil water storage and ET in a tropical forest ecosystem, particularly under drought conditions, suggesting potential species that may be more effective in recovering degraded areas.

12 May 2026 in Brazilian Journal of Biometrics
Verified
Journal Article
Open access
MOMomate Ossifo
Momate Ossifo
SMSeverino José Macôo
Severino José Macôo
Adérito Celestino CauAdérito Celestino Cau
Adérito Celestino Cau
RSRafaela de Carvalho Salvador
Rafaela de Carvalho Salvador
NGNatiele de Almeida Gonzaga
Natiele de Almeida Gonzaga
JSJoão Domingos Scalon
João Domingos Scalon
Tropical forests play a crucial role in regulating the climate and maintaining the global carbon cycle. Carbon is primarily stored in plant biomass. In Africa, these forests still present significant uncertainty in the carbon balance. This is due to scarce inventories and a lack of continuous monitoring. Mopane forests (Colophospermum mopane) are typical of dry tropical regions and have important ecological and socioeconomic roles. They are widely used by local communities. In Mozambique, charcoal production mainly drives degradation of these forests and loss of aboveground carbon (AGC). The aim of this study is to analyze aboveground carbon stock in Mopane forests in the Chicualacuala District, Southern Mozambique. The data used refer to georeferenced tree locations in a fragment of native forest. Aboveground biomass was individually estimated through a pantropical equation, using as predictor variables: diameter at breast height (DBH), total height, and basic wood density. From the biomass estimates, AGC was calculated and subsequently analyzed using geostatistical methods such as variograms and ordinary kriging. The results indicated that the ordinary kriging model partially captured the spatial pattern of AGC and, as evidenced by cross-validation, showed a moderate positive correlation (54.66%) between observed and predicted values. This level of correlation suggests that while the model provides a reasonable prediction of AGC, there is still considerable unexplained variability, which highlights areas for further model refinement or additional data collection.