Forests play an important role in reducing greenhouse gases in the atmosphere, thereby mitigating the impact of climate change. Estimating the accumulated biomass in a forest ecosystem is important for assessing the productivity and sustainability of the forest. Allometric models for above ground biomass (AGB) are linear regression equations based on the relationships between biomass and diameter at breast height (DBH), tree height (H), and/or wood density. This study estimates AGB in the Colo-I-Suva Forest Park by applying the allometry of Chave et al (2005) and the diameter: height ratio derived from Payton & Weaver (2011) for height estimation in a plot of 20

Allometric equations are used to estimate accurate biomass and carbon stock of forests. However, in Ethiopia only few allometric equations as compared to its floral diversity and species-specific allometric equations for Acacia species are still not developed in Ethiopia. The numbers of tree marked for sampling are Fifty-four (54) using preferential sampling. Diameter at breast height, wood density and tree height were collected as independent variables to predict species specific dry biomass of Acacia species. The new species-specific allometric models have been performed using linear regression analysis in the R software. The Above ground biomass (AGB) have been validated using quantitative statically using the pantropic model. Six candidate models have been developed for each species and four best models for each species of dry biomass was selected based on goodness-of-fit statistics and equation performance analysis of the candidate models. The best model for predicting above ground biomass for Acacia seyal is 0.20636*((DBH²)Hρ) ^0.53167, for Acacia polyacantha is 7.26982((DBH)²Hρ)^0.21750, for Acacia ethibcia is 29.01898*((DBH)²Hρ)^0.21518 and for Acacia toritolis is 3.82427*((DBH)²Hρ)^0.16748. The selected models are the best performing (P> 0.01) and higher adjusted R² (>80%) and has lower Akaike’s Information Criteria (AIC) and residual standard error (RSE) values as comparing the rest of the model. The validation of new developed biomass model using Tukey test indicated that significant variation of mean biomass (P<0.05) between the new developed model and the generalized model. The statistics model performance analysis of Nash-Sutcliffe efficiency (NSE) value is approaching to one, indicating that the new developed model has better performance model as compared with generalized model. Moreover, the percent bias of the new developed models is close to zero which indicates that the site-specific biomass models have more accurate estimator and the generalized biomass models have overestimated biomass for the four Acacia species.

Understory plants are important components of forest ecosystem productivity and diversity. Compared to biomass models of overstory canopy trees, few are available for understory saplings and shrubs and therefore their roles in estimation of forest carbon pools are often ignored. In this study, we harvested 24 understory species including 4 saplings, 9 tree-like shrubs and 11 typical shrubs in coniferous and broadleaved mixed forest in northeastern China and developed the best fit allometric equations of above- and below-ground and total biomass by species-specific or multispecies using morphological measurements of basal diameter, height and crown area as independent variables. The result showed that single basal diameter, height or crown area had good explanatory power for both species-specific and multispecies (p<0.001). The best-fit models included only basal diameter in sapling and tree-like shrubs, and combinations of crown area, height, and basal diameter in typical shrubs. The logarithmic model was most desired among the 4 model forms of linear, quadratic, multiple linear and logarithmic, for species-specific and multispecies. The models we developed should help the estimation of forest ecosystem carbon stocks, especially for belowground component, and provide tools for quantification of individual species biomass of understory plants.

Why animals sleep is an outstanding open question. Information about the toxic byproducts of aerobic cellular respiration along with the analysis of patterns in animal size, sleep needs, dietary-type, metabolism, number of heart beats, transportation-network design, and transportation energetics/dynamics suggest that the function of sleep is to maximize the time an animal has to perform its life functions given the finite and constant number of lifetime heart beats it has. Sleep slows down metabolism, and the heart rate, thereby decreasing the load of toxic reactive oxygen species in the cell and extending the cell’s lifetime/proper-functioning. I argue that this is used to maximize the time an animal spends in its ‘effective environment’, which is defined as the period in the light cycle (day or night) where the essential life-functions of that animal (like finding resources, finding sex, hunting) are better achieved. Larger, slow-metabolizing animals need less sleep because their large-bodily-networks and slow metabolisms keep their heart rates relatively low, resulting in a lower rate of oxidative damage, and more relative time in the ‘effective environment’ to get their essential life-functions accomplished.

Mangroves play an important role as a carbon sink and in mitigation of climate change. This study aimed to assess the anthropogenic activities, water and soil quality, mangrove diversity, and carbon sequestration potential of mangrove trees in the Kaingen River, Kawit, Cavite. The sampling period was conducted from November 2022 to March 2023 with the established three sampling sites. The DENR Administrative Order (DAO)- 2016-08 was used as a standard for water quality parameters, except for phosphates which used DAO-2021-19. The soil parameters were identified using the soil test kit from the Bureau of Soil and Water Management (BSWM) and at the BSWM laboratory. Mangrove species were identified using The Field Guide for Philippine Mangroves and were verified by experts. The carbon sequestration potential was obtained using an allometric equation for Southeast Asian mangroves. There are three mangrove species found in Kaingen Riverine such as Rhizophora mucronata, Avicennia alba, and Xylocarpus granatum. Based on species importance value Rhizophora mucronata is the dominant mangrove species. The result for carbon sequestration of each mangrove species showed that Rhizophora mucronata yielded the highest carbon stored (35.16 tC/ha) and carbon sequestered (128.92 tCO₂/ha). Among all the sampling sites, site 3 yielded the highest carbon stored (30.76 tC/ha) and carbon sequestered (112.81 tCO₂/ha) in Kaingen River. Overall, the results of the study showed that Kaingen River can potentially store carbon at 71.89 tC/ha and CO2 sequestered at 263.62 tCO₂/ha. This urges to practice conservation and protection measures for the mangroves forest of Kaingen River.

This study aimed to develop a carbon yield model for a Makino bamboo plantation and addressed a monoculture Makino bamboo plantation in central Taiwan. We established a long-term trial to monitor the stand dynamic following thinning. A total of 16 plots were installed based on four thinning treatments, each sharing four plots. After thinning, data were collected from two investigations, resulting in 32 records. We calculated the number of culms (N), mean diameter at breast height (MDBH), and basal area (BA) for each record. An allometric function developed by previous research was used to predict aboveground biomass and to obtain aboveground carbon storage (AGCS). The model used the N, MDBH, and BA with various types (reciprocal and natural logarithm types) as independent variables to predict ln(AGCS) by the stepwise method. According to the Radj2 value, the best predictive models were: ln(ABCS) = -0.004 + 0.820 ln(MDBH) + 1.008 ln(BA), and ln(ABCS) = 7.550 - 0.820 ln(N) + 1.828 ln(BA). From these two models, we found the factors, either BA and MDBH or BA and N, can effectively predict AGCS. It indicated that stand density was critical in affecting AGCS, especially the variable BA.

The negative impact of drought on plant growth may be modified by different availability of mineral nutrients and by their adaptation to different local habitat conditions. In this study, we examine the impact of drought, fertilization with phosphorus and provenance, as well as their interactions, on the growth and allometric growth relationships between belowground and aboveground organs of common beech (Fagus sylvatica) and sessile oak (Quercus petraea). The research was conducted on saplings originating from two mature mixed stands (dry and wet provenances) dominated by these species. In the common garden experiment, saplings were exposed to regular watering and drought in interaction with moderate and high phosphorus concentrations in the growing substrate (achieved by phosphorus fertilization). Obtained results indicate negative impact of drought and phosphorus fertilization on the growth of both species. In common beech, phosphorus fertilization had a negative impact on its adaptive capacity to drought, because of the unfavourable ratios between fine root mass and mass of other organs. The sessile oak provenances under the impact of drought showed a different root collar diameter/stem height increment ratio, which indicates its different phenotypic plasticity as a consequence of adaptation to different frequencies of dry periods in their natural habitats.