Biomass of mesoplankton and macroplankton in the central tropical and equatorial regions of the Pacific Ocean

Collections made with 150 l sampling bottles and BR 113/140 nets, as well as direct counts from the Mir submersible are used to analyze vertical distribution of total biomass of meso- and macroplankton and biomass distributions of their main component groups in the central oligotrophic regions of the North Pacific. Biomass of mesoplankton in the upper 200 m layer ranges from 3.1 to 8.6 g/m**2, but sometimes it increases up to as much as 98 g/m**2 in local population explosions of salps. Jellies predominate in macroplankton at depths of up to 2-3 km, contributing 97-98% of live weight and 30-70% of biomass as organic carbon. In importance they are followed by micronecton fishes (up to 40% of organic carbon). Contributions of other groups countable from the submersible were negligible. Distributions of species at particular stations are discussed.

Verical distribution of Copepoda biomass in the Northwest and tropical Pacific

Quantitative analysis of vertical distribution of copepod families revealed a pattern of variation with depth (from the surface to the greatest ocean depths) in the trophic structure of this taxocenosis in the pelagic Pacific.

Lupane triterpenes from the leaves of the tropical rain forest tree Hopea odorata Roxb. and their cytotoxic activities, supplementary information

Chromatographic fractionation of the cytotoxic n-hexane extract of Hopea odorata Roxb. leaves led to the isolation of eight lupane triterpenes, which constitutes the first report of lupane-type triterpenes from this plant source. Furthermore, 3,30-dioxolup-20(29)-en-28-oic acid (6) was isolated for the first time from a natural source. Their structures were determined on the basis of spectroscopic methods, including 2D NMR analysis, and by comparison of their spectral data with literature values. Complete NMR assignments of the 1H and 13C NMR data were achieved for all compounds. Finally, the cytotoxic activities of the isolated compounds against four human cell lines (PC3, MDA-MB-231, HT-29 and HCT116) was also reported.

Geografía del carbono en alta resolución en bosque tropical amazónico del Ecuador mediante sensores aerotransportados

La estimación de la biomasa de la vegetación terrestre en bosque tropical no sólo es un área de investigación en rápida expansión, sino también es un tema de gran interés para reducir las emisiones de carbono asociadas a la deforestación y la degradación forestal (REDD+). Las estimaciones de densidad de carbono sobre el suelo (ACD) en base a inventarios de campo y datos provenientes de sensores aerotransportados, en especial con sensores LiDAR, han conducido a un progreso sustancial en el cartografiado a gran escala de las reservas de carbono forestal. Sin embargo, estos mapas de carbono tienen incertidumbres considerables, asociadas generalmente al proceso de calibración del modelo de regresión utilizado para producir los mapas. En esta tesis se establece una metodología para la calibración y validación de un modelo general de estimación de ACD usando LiDAR en un sector del Parque Nacional Yasuní en Ecuador. En el proceso de calibración del modelo se considera el tamaño y la ubicación de las parcelas, la influencia de la topografía y la distribución espacial de la biomasa. Para el análisis de los datos se utilizan técnicas geoestadísticas en combinación con variables geomorfométricas derivadas de datos LiDAR, y se propone un esquema de muestreo estratificado por posiciones topográficas (valle, ladera y cima). La validación del modelo general para toda la zona de estudio presentó valores de RMSE = 5.81 Mg C ha-1, R2 = 0.94 y sesgo = 0.59, mientras que, al considerar las posiciones topográficas, el modelo presentó valores de RMSE = 1.67 Mg C ha-1, R2 = 0.98 y sesgo = 0.23 para el valle; RMSE = 3.13 Mg C ha-1, R2 = 0.98 y sesgo = - 0.34 para la ladera; y RMSE = 2.33 Mg C ha-1, R2 = 0.97 y sesgo = 0.74 para la cima. Los resultados obtenidos demuestran que la metodología de muestreo estratificado por posiciones topográficas propuesto, permite calibrar de manera efectiva el modelo general con las estimaciones de ACD en campo, logrando reducir el RMSE y el sesgo. Los resultados muestran el potencial de los datos LiDAR para caracterizar la estructura vertical de la vegetación en un bosque altamente diverso, permitiendo realizar estimaciones precisas de ACD, y conocer patrones espaciales continuos de la distribución de la biomasa aérea y del contenido de carbono en la zona de estudio. ABSTRACT Estimating biomass of terrestrial vegetation in tropical forest is not only a rapidly expanding research area, but also a subject of tremendous interest for reducing carbon emissions associated with deforestation and forest degradation (REDD+). The aboveground carbon density estimates (ACD) based on field inventories and airborne sensors, especially LiDAR sensors have led to a substantial progress in large-scale mapping of forest carbon stocks. However, these carbon maps have considerable uncertainties generally associated with the calibration of the regression model used to produce these maps. This thesis establishes a methodology for calibrating and validating a general ACD estimation model using LiDAR in Ecuador´s Yasuní National Park. The size and location of the plots are considered in the model calibration phase as well as the influence of topography and spatial distribution of biomass. Geostatistical analysis techniques are used in combination with geomorphometrics variables derived from LiDAR data, and then a stratified sampling scheme considering topographic positions (valley, slope and ridge) is proposed. The validation of the general model for the study area showed values of RMSE = 5.81 Mg C ha-1, R2 = 0.94 and bias = 0.59, while considering the topographical positions, the model showed values of RMSE = 1.67 Mg C ha-1, R2 = 0.98 and bias = 0.23 for the valley; RMSE = 3.13 Mg C ha-1, R2 = 0.98 and bias = - 0.34 for the slope; and RMSE = 2.33 Mg C ha-1, R2 = 0.97 and bias = 0.74 for the ridge. The results show that the stratified sampling methodology taking into account topographic positions, effectively calibrates the general model with field estimates of ACD, reducing RMSE and bias. The results show the potential of LiDAR data to characterize the vertical structure of vegetation in a highly diverse forest, allowing accurate estimates of ACD, and knowing continuous spatial patterns of biomass distribution and carbon stocks in the study area.